Bump up version to v2.3.0rc3

- Also changed the "latest" tag to "latest-2.3" to avoid interfering
  with the 2.2.5 update script, which fetches "latest"

.dockerignore

@@ -1,9 +1,20 @@
+# use this file as a whitelist
 *
+!backend
 !invokeai
+!ldm
 !pyproject.toml
-!docker/docker-entrypoint.sh
-!LICENSE
+!README.md
+!scripts
 
-**/node_modules
+# Guard against pulling in any models that might exist in the directory tree
+**/*.pt*
+**/*.ckpt
+
+# whitelist frontend, but ignore node_modules
+invokeai/frontend/node_modules
+
+# ignore python cache
 **/__pycache__
-**/*.egg-info
+**/*.py[cod]
+**/*.egg-info

.git-blame-ignore-revs

@@ -1 +0,0 @@
-b3dccfaeb636599c02effc377cdd8a87d658256c

.github/CODEOWNERS

@@ -1,34 +1,7 @@
-# continuous integration
-/.github/workflows/  @lstein @blessedcoolant
-
-# documentation
-/docs/ @lstein @blessedcoolant @hipsterusername
-/mkdocs.yml @lstein  @blessedcoolant
-
-# nodes
-/invokeai/app/ @Kyle0654 @blessedcoolant @psychedelicious @brandonrising 
-
-# installation and configuration
-/pyproject.toml  @lstein @blessedcoolant
-/docker/  @lstein @blessedcoolant
-/scripts/ @ebr @lstein
-/installer/ @lstein @ebr
-/invokeai/assets @lstein @ebr
-/invokeai/configs @lstein
-/invokeai/version @lstein @blessedcoolant
-
-# web ui
-/invokeai/frontend @blessedcoolant @psychedelicious @lstein @maryhipp
-/invokeai/backend @blessedcoolant @psychedelicious @lstein @maryhipp
-
-# generation, model management, postprocessing
-/invokeai/backend  @damian0815 @lstein @blessedcoolant @gregghelt2 @StAlKeR7779 @brandonrising
-
-# front ends
-/invokeai/frontend/CLI @lstein
-/invokeai/frontend/install @lstein @ebr  
-/invokeai/frontend/merge @lstein @blessedcoolant 
-/invokeai/frontend/training @lstein @blessedcoolant 
-/invokeai/frontend/web @psychedelicious @blessedcoolant @maryhipp 
-
-
+ldm/invoke/pngwriter.py @CapableWeb
+ldm/invoke/server_legacy.py @CapableWeb
+scripts/legacy_api.py @CapableWeb
+tests/legacy_tests.sh @CapableWeb
+installer/ @ebr
+.github/workflows/ @mauwii
+docker/ @mauwii

.github/ISSUE_TEMPLATE/BUG_REPORT.yml

@@ -65,16 +65,6 @@ body:
       placeholder: 8GB
     validations:
       required: false
-      
-  - type: input
-    id: version-number
-    attributes:
-      label: What version did you experience this issue on?
-      description: |
-        Please share the version of Invoke AI that you experienced the issue on. If this is not the latest version, please update first to confirm the issue still exists. If you are testing main, please include the commit hash instead.
-      placeholder: X.X.X
-    validations:
-      required: true
 
   - type: textarea
     id: what-happened

.github/stale.yaml

@@ -1,19 +0,0 @@
-# Number of days of inactivity before an issue becomes stale
-daysUntilStale: 28
-# Number of days of inactivity before a stale issue is closed
-daysUntilClose: 14
-# Issues with these labels will never be considered stale
-exemptLabels:
-  - pinned
-  - security
-# Label to use when marking an issue as stale
-staleLabel: stale
-# Comment to post when marking an issue as stale. Set to `false` to disable
-markComment: >
-  This issue has been automatically marked as stale because it has not had
-  recent activity. It will be closed if no further activity occurs. Please
-  update the ticket if this is still a problem on the latest release.
-# Comment to post when closing a stale issue. Set to `false` to disable
-closeComment: >
-  Due to inactivity, this issue has been automatically closed. If this is
-  still a problem on the latest release, please recreate the issue.

.github/workflows/build-container.yml

@@ -3,20 +3,8 @@ on:
   push:
     branches:
       - 'main'
-    paths:
-      - 'pyproject.toml'
-      - '.dockerignore'
-      - 'invokeai/**'
-      - 'docker/Dockerfile'
-      - 'docker/docker-entrypoint.sh'
-      - 'workflows/build-container.yml'
     tags:
-      - 'v*'
-  workflow_dispatch:
-
-permissions:
-  contents: write
-  packages: write
+      - 'v*.*.*'
 
 jobs:
   docker:
@@ -24,27 +12,26 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        gpu-driver:
-        - cuda
-        - cpu
-        - rocm
+        flavor:
+          - amd
+          - cuda
+          - cpu
+        include:
+          - flavor: amd
+            pip-extra-index-url: 'https://download.pytorch.org/whl/rocm5.2'
+            dockerfile: docker/Dockerfile
+            platforms: linux/amd64,linux/arm64
+          - flavor: cuda
+            pip-extra-index-url: ''
+            dockerfile: docker/Dockerfile
+            platforms: linux/amd64,linux/arm64
+          - flavor: cpu
+            pip-extra-index-url: 'https://download.pytorch.org/whl/cpu'
+            dockerfile: docker/Dockerfile
+            platforms: linux/amd64,linux/arm64
     runs-on: ubuntu-latest
-    name: ${{ matrix.gpu-driver }}
-    env:
-      # torch/arm64 does not support GPU currently, so arm64 builds
-      # would not be GPU-accelerated.
-      # re-enable arm64 if there is sufficient demand.
-      # PLATFORMS: 'linux/amd64,linux/arm64'
-      PLATFORMS: 'linux/amd64'
+    name: ${{ matrix.flavor }}
     steps:
-      - name: Free up more disk space on the runner
-        # https://github.com/actions/runner-images/issues/2840#issuecomment-1284059930
-        run: |
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf "$AGENT_TOOLSDIRECTORY"
-          sudo swapoff /mnt/swapfile
-          sudo rm -rf /mnt/swapfile
-
       - name: Checkout
         uses: actions/checkout@v3
 
@@ -53,27 +40,23 @@ jobs:
         uses: docker/metadata-action@v4
         with:
           github-token: ${{ secrets.GITHUB_TOKEN }}
-          images: |
-            ghcr.io/${{ github.repository }}
-            ${{ env.DOCKERHUB_REPOSITORY }}
+          images: ghcr.io/${{ github.repository }}
           tags: |
             type=ref,event=branch
             type=ref,event=tag
-            type=pep440,pattern={{version}}
-            type=pep440,pattern={{major}}.{{minor}}
-            type=pep440,pattern={{major}}
-            type=sha,enable=true,prefix=sha-,format=short
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+            type=semver,pattern={{major}}
+            type=sha
           flavor: |
-            latest=${{ matrix.gpu-driver == 'cuda' && github.ref == 'refs/heads/main' }}
-            suffix=-${{ matrix.gpu-driver }},onlatest=false
+            latest=${{ matrix.flavor == 'cuda' && github.ref == 'refs/heads/main' }}
+            suffix=${{ matrix.flavor }},onlatest=false
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
-        with:
-          platforms: ${{ env.PLATFORMS }}
 
       - name: Login to GitHub Container Registry
         if: github.event_name != 'pull_request'
@@ -83,33 +66,25 @@ jobs:
           username: ${{ github.repository_owner }}
           password: ${{ secrets.GITHUB_TOKEN }}
 
-      # - name: Login to Docker Hub
-      #   if: github.event_name != 'pull_request' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: docker/login-action@v2
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
-
       - name: Build container
-        id: docker_build
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v3
         with:
           context: .
-          file: docker/Dockerfile
-          platforms: ${{ env.PLATFORMS }}
-          push: ${{ github.ref == 'refs/heads/main' || github.ref_type == 'tag' }}
+          file: ${{ matrix.dockerfile }}
+          platforms: ${{ matrix.platforms }}
+          push: ${{ github.event_name != 'pull_request' }}
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-          cache-from: |
-            type=gha,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
-            type=gha,scope=main-${{ matrix.gpu-driver }}
-          cache-to: type=gha,mode=max,scope=${{ github.ref_name }}-${{ matrix.gpu-driver }}
+          build-args: PIP_EXTRA_INDEX_URL=${{ matrix.pip-extra-index-url }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
 
-      # - name: Docker Hub Description
-      #   if: github.ref == 'refs/heads/main' || github.ref == 'refs/tags/*' && vars.DOCKERHUB_REPOSITORY != ''
-      #   uses: peter-evans/dockerhub-description@v3
-      #   with:
-      #     username: ${{ secrets.DOCKERHUB_USERNAME }}
-      #     password: ${{ secrets.DOCKERHUB_TOKEN }}
-      #     repository: ${{ vars.DOCKERHUB_REPOSITORY }}
-      #     short-description: ${{ github.event.repository.description }}
+      - name: Output image, digest and metadata to summary
+        run: |
+          {
+          echo imageid: "${{ steps.docker_build.outputs.imageid }}"
+          echo digest: "${{ steps.docker_build.outputs.digest }}"
+          echo labels: "${{ steps.meta.outputs.labels }}"
+          echo tags: "${{ steps.meta.outputs.tags }}"
+          echo version: "${{ steps.meta.outputs.version }}"
+          } >> "$GITHUB_STEP_SUMMARY"

.github/workflows/close-inactive-issues.yml

@@ -1,27 +0,0 @@
-name: Close inactive issues
-on:
-  schedule:
-    - cron: "00 6 * * *"
-
-env:
-  DAYS_BEFORE_ISSUE_STALE: 14
-  DAYS_BEFORE_ISSUE_CLOSE: 28
-
-jobs:
-  close-issues:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/stale@v5
-        with:
-          days-before-issue-stale: ${{ env.DAYS_BEFORE_ISSUE_STALE }}
-          days-before-issue-close: ${{ env.DAYS_BEFORE_ISSUE_CLOSE }}
-          stale-issue-label: "Inactive Issue"
-          stale-issue-message: "There has been no activity in this issue for ${{ env.DAYS_BEFORE_ISSUE_STALE }} days. If this issue is still being experienced, please reply with an updated confirmation that the issue is still being experienced with the latest release."
-          close-issue-message: "Due to inactivity, this issue was automatically closed. If you are still experiencing the issue, please recreate the issue."
-          days-before-pr-stale: -1
-          days-before-pr-close: -1
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          operations-per-run: 500

.github/workflows/lint-frontend.yml

@@ -3,22 +3,14 @@ name: Lint frontend
 on:
   pull_request:
     paths:
-      - 'invokeai/frontend/web/**'
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
+      - 'invokeai/frontend/**'
   push:
-    branches:
-      - 'main'
     paths:
-      - 'invokeai/frontend/web/**'
-  merge_group:
-  workflow_dispatch:
+      - 'invokeai/frontend/**'
 
 defaults:
   run:
-    working-directory: invokeai/frontend/web
+    working-directory: invokeai/frontend
 
 jobs:
   lint-frontend:
@@ -31,7 +23,7 @@ jobs:
           node-version: '18'
       - uses: actions/checkout@v3
       - run: 'yarn install --frozen-lockfile'
-      - run: 'yarn run lint:tsc'
-      - run: 'yarn run lint:madge'
-      - run: 'yarn run lint:eslint'
-      - run: 'yarn run lint:prettier'
+      - run: 'yarn tsc'
+      - run: 'yarn run madge'
+      - run: 'yarn run lint --max-warnings=0'
+      - run: 'yarn run prettier --check'

.github/workflows/mkdocs-material.yml

@@ -2,19 +2,13 @@ name: mkdocs-material
 on:
   push:
     branches:
-      - 'refs/heads/v2.3'
-
-permissions:
-    contents: write
+      - 'main'
+      - 'development'
 
 jobs:
   mkdocs-material:
     if: github.event.pull_request.draft == false
     runs-on: ubuntu-latest
-    env:
-      REPO_URL: '${{ github.server_url }}/${{ github.repository }}'
-      REPO_NAME: '${{ github.repository }}'
-      SITE_URL: 'https://${{ github.repository_owner }}.github.io/InvokeAI'
     steps:
       - name: checkout sources
         uses: actions/checkout@v3
@@ -25,15 +19,11 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: '3.10'
-          cache: pip
-          cache-dependency-path: pyproject.toml
 
       - name: install requirements
-        env:
-          PIP_USE_PEP517: 1
         run: |
           python -m \
-            pip install ".[docs]"
+            pip install -r docs/requirements-mkdocs.txt
 
       - name: confirm buildability
         run: |

.github/workflows/pypi-release.yml

@@ -1,41 +0,0 @@
-name: PyPI Release
-
-on:
-  push:
-    paths:
-      - 'invokeai/version/invokeai_version.py'
-  workflow_dispatch:
-
-jobs:
-  release:
-    if: github.repository == 'invoke-ai/InvokeAI'
-    runs-on: ubuntu-22.04
-    env:
-      TWINE_USERNAME: __token__
-      TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
-      TWINE_NON_INTERACTIVE: 1
-    steps:
-      - name: checkout sources
-        uses: actions/checkout@v3
-
-      - name: install deps
-        run: pip install --upgrade build twine
-
-      - name: build package
-        run: python3 -m build
-
-      - name: check distribution
-        run: twine check dist/*
-
-      - name: check PyPI versions
-        if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/v2.3'
-        run: |
-          pip install --upgrade requests
-          python -c "\
-          import scripts.pypi_helper; \
-          EXISTS=scripts.pypi_helper.local_on_pypi(); \
-          print(f'PACKAGE_EXISTS={EXISTS}')" >> $GITHUB_ENV
-
-      - name: upload package
-        if: env.PACKAGE_EXISTS == 'False' && env.TWINE_PASSWORD != ''
-        run: twine upload dist/*

.github/workflows/test-invoke-pip-skip.yml

@@ -1,50 +0,0 @@
-name: Test invoke.py pip
-
-# This is a dummy stand-in for the actual tests
-# we don't need to run python tests on non-Python changes
-# But PRs require passing tests to be mergeable
-
-on:
-  pull_request:
-    paths:
-      - '**'
-      - '!pyproject.toml'
-      - '!invokeai/**'
-      - '!tests/**'
-      - 'invokeai/frontend/web/**'
-  merge_group:
-  workflow_dispatch:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  matrix:
-    if: github.event.pull_request.draft == false
-    strategy:
-      matrix:
-        python-version:
-          - '3.10'
-        pytorch:
-          - linux-cuda-11_7
-          - linux-rocm-5_2
-          - linux-cpu
-          - macos-default
-          - windows-cpu
-        include:
-          - pytorch: linux-cuda-11_7
-            os: ubuntu-22.04
-          - pytorch: linux-rocm-5_2
-            os: ubuntu-22.04
-          - pytorch: linux-cpu
-            os: ubuntu-22.04
-          - pytorch: macos-default
-            os: macOS-12
-          - pytorch: windows-cpu
-            os: windows-2022
-    name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
-    runs-on: ${{ matrix.os }}
-    steps:
-      - name: skip
-        run: echo "no build required"

.github/workflows/test-invoke-pip.yml

@@ -3,26 +3,15 @@ on:
   push:
     branches:
       - 'main'
-    paths:
-      - 'pyproject.toml'
-      - 'invokeai/**'
-      - '!invokeai/frontend/web/**'
   pull_request:
-    paths:
-      - 'pyproject.toml'
-      - 'invokeai/**'
-      - 'tests/**'
-      - '!invokeai/frontend/web/**'
     types:
       - 'ready_for_review'
       - 'opened'
       - 'synchronize'
-  merge_group:
-  workflow_dispatch:
 
 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
+   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+   cancel-in-progress: true
 
 jobs:
   matrix:
@@ -33,12 +22,19 @@ jobs:
           # - '3.9'
           - '3.10'
         pytorch:
+          # - linux-cuda-11_6
           - linux-cuda-11_7
           - linux-rocm-5_2
           - linux-cpu
           - macos-default
           - windows-cpu
+          # - windows-cuda-11_6
+          # - windows-cuda-11_7
         include:
+          # - pytorch: linux-cuda-11_6
+          #   os: ubuntu-22.04
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $GITHUB_ENV
           - pytorch: linux-cuda-11_7
             os: ubuntu-22.04
             github-env: $GITHUB_ENV
@@ -56,68 +52,95 @@ jobs:
           - pytorch: windows-cpu
             os: windows-2022
             github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_6
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu116'
+          #   github-env: $env:GITHUB_ENV
+          # - pytorch: windows-cuda-11_7
+          #   os: windows-2022
+          #   extra-index-url: 'https://download.pytorch.org/whl/cu117'
+          #   github-env: $env:GITHUB_ENV
     name: ${{ matrix.pytorch }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
-    env:
-      PIP_USE_PEP517: '1'
     steps:
       - name: Checkout sources
         id: checkout-sources
         uses: actions/checkout@v3
 
-      - name: set test prompt to main branch validation
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
-
       - name: setup python
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: pip
-          cache-dependency-path: pyproject.toml
+
+      - name: Set Cache-Directory Windows
+        if: runner.os == 'Windows'
+        id: set-cache-dir-windows
+        run: |
+          echo "CACHE_DIR=$HOME\invokeai\models" >> ${{ matrix.github-env }}
+          echo "PIP_NO_CACHE_DIR=1" >> ${{ matrix.github-env }}
+
+      - name: Set Cache-Directory others
+        if: runner.os != 'Windows'
+        id: set-cache-dir-others
+        run: echo "CACHE_DIR=$HOME/invokeai/models" >> ${{ matrix.github-env }}
+
+      - name: set test prompt to main branch validation
+        if: ${{ github.ref == 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
+
+      - name: set test prompt to Pull Request validation
+        if: ${{ github.ref != 'refs/heads/main' }}
+        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
 
       - name: install invokeai
         env:
           PIP_EXTRA_INDEX_URL: ${{ matrix.extra-index-url }}
         run: >
           pip3 install
+          --use-pep517
           --editable=".[test]"
 
       - name: run pytest
-        id: run-pytest
         run: pytest
 
-      # - name: run invokeai-configure
-      #   env:
-      #     HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-      #   run: >
-      #     invokeai-configure
-      #     --yes
-      #     --default_only
-      #     --full-precision
-      #   # can't use fp16 weights without a GPU
+      - name: Use Cached models
+        id: cache-sd-model
+        uses: actions/cache@v3
+        env:
+          cache-name: huggingface-models
+        with:
+          path: ${{ env.CACHE_DIR }}
+          key: ${{ env.cache-name }}
+          enableCrossOsArchive: true
 
-      # - name: run invokeai
-      #   id: run-invokeai
-      #   env:
-      #     # Set offline mode to make sure configure preloaded successfully.
-      #     HF_HUB_OFFLINE: 1
-      #     HF_DATASETS_OFFLINE: 1
-      #     TRANSFORMERS_OFFLINE: 1
-      #     INVOKEAI_OUTDIR: ${{ github.workspace }}/results
-      #   run: >
-      #     invokeai
-      #     --no-patchmatch
-      #     --no-nsfw_checker
-      #     --precision=float32
-      #     --always_use_cpu
-      #     --use_memory_db
-      #     --outdir ${{ env.INVOKEAI_OUTDIR }}/${{ matrix.python-version }}/${{ matrix.pytorch }}
-      #     --from_file ${{ env.TEST_PROMPTS }}
+      - name: run invokeai-configure
+        id: run-preload-models
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
+        run: >
+          invokeai-configure
+          --yes
+          --default_only
+          --full-precision
+        # can't use fp16 weights without a GPU
 
-      # - name: Archive results
-      #   env:
-      #     INVOKEAI_OUTDIR: ${{ github.workspace }}/results
-      #   uses: actions/upload-artifact@v3
-      #   with:
-      #     name: results
-      #     path: ${{ env.INVOKEAI_OUTDIR }}
+      - name: Run the tests
+        if: runner.os != 'Windows'
+        id: run-tests
+        env:
+          # Set offline mode to make sure configure preloaded successfully.
+          HF_HUB_OFFLINE: 1
+          HF_DATASETS_OFFLINE: 1
+          TRANSFORMERS_OFFLINE: 1
+        run: >
+          invokeai
+          --no-patchmatch
+          --no-nsfw_checker
+          --from_file ${{ env.TEST_PROMPTS }}
+
+      - name: Archive results
+        id: archive-results
+        uses: actions/upload-artifact@v3
+        with:
+          name: results_${{ matrix.pytorch }}_${{ matrix.python-version }}
+          path: ${{ env.INVOKEAI_ROOT }}/outputs

.gitignore

@@ -1,5 +1,4 @@
 # ignore default image save location and model symbolic link
-.idea/
 embeddings/
 outputs/
 models/ldm/stable-diffusion-v1/model.ckpt
@@ -9,8 +8,6 @@ models/ldm/stable-diffusion-v1/model.ckpt
 configs/models.user.yaml
 config/models.user.yml
 invokeai.init
-.version
-.last_model
 
 # ignore the Anaconda/Miniconda installer used while building Docker image
 anaconda.sh
@@ -34,7 +31,7 @@ __pycache__/
 .Python
 build/
 develop-eggs/
-# dist/
+dist/
 downloads/
 eggs/
 .eggs/
@@ -65,21 +62,17 @@ pip-delete-this-directory.txt
 htmlcov/
 .tox/
 .nox/
-.coveragerc
 .coverage
 .coverage.*
 .cache
 nosetests.xml
 coverage.xml
-cov.xml
 *.cover
 *.py,cover
 .hypothesis/
 .pytest_cache/
-.pytest.ini
 cover/
 junit/
-notes/
 
 # Translations
 *.mo
@@ -202,11 +195,8 @@ checkpoints
 # If it's a Mac
 .DS_Store
 
-invokeai/frontend/yarn.lock
-invokeai/frontend/node_modules
-
 # Let the frontend manage its own gitignore
-!invokeai/frontend/web/*
+!invokeai/frontend/*
 
 # Scratch folder
 .scratch/
@@ -221,6 +211,11 @@ gfpgan/
 # config file (will be created by installer)
 configs/models.yaml
 
+# weights (will be created by installer)
+models/ldm/stable-diffusion-v1/*.ckpt
+models/clipseg
+models/gfpgan
+
 # ignore initfile
 .invokeai
 
@@ -235,3 +230,6 @@ installer/install.bat
 installer/install.sh
 installer/update.bat
 installer/update.sh
+
+# no longer stored in source directory
+models

LICENSE

@@ -1,176 +1,21 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
+MIT License
 
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+Copyright (c) 2022 InvokeAI Team
 
-   1. Definitions.
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
 
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
 
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
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+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,11 +1,8 @@
 <div align="center">
 
-![project hero](https://github.com/invoke-ai/InvokeAI/assets/31807370/1a917d94-e099-4fa1-a70f-7dd8d0691018)
-
-# Invoke AI - Generative AI for Professional Creatives
-## Professional Creative Tools for Stable Diffusion, Custom-Trained Models, and more. 
-  To learn more about Invoke AI, get started instantly, or implement our Business solutions, visit [invoke.ai](https://invoke.ai)
+![project logo](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/invoke_ai_banner.png)
 
+# InvokeAI: A Stable Diffusion Toolkit
 
 [![discord badge]][discord link]
 
@@ -13,10 +10,10 @@
 
 [![CI checks on main badge]][CI checks on main link] [![latest commit to main badge]][latest commit to main link]
 
-[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link] [![translation status badge]][translation status link]
+[![github open issues badge]][github open issues link] [![github open prs badge]][github open prs link]
 
 [CI checks on main badge]: https://flat.badgen.net/github/checks/invoke-ai/InvokeAI/main?label=CI%20status%20on%20main&cache=900&icon=github
-[CI checks on main link]:https://github.com/invoke-ai/InvokeAI/actions?query=branch%3Amain
+[CI checks on main link]: https://github.com/invoke-ai/InvokeAI/actions/workflows/test-invoke-conda.yml
 [discord badge]: https://flat.badgen.net/discord/members/ZmtBAhwWhy?icon=discord
 [discord link]: https://discord.gg/ZmtBAhwWhy
 [github forks badge]: https://flat.badgen.net/github/forks/invoke-ai/InvokeAI?icon=github
@@ -31,191 +28,51 @@
 [latest commit to main link]: https://github.com/invoke-ai/InvokeAI/commits/main
 [latest release badge]: https://flat.badgen.net/github/release/invoke-ai/InvokeAI/development?icon=github
 [latest release link]: https://github.com/invoke-ai/InvokeAI/releases
-[translation status badge]: https://hosted.weblate.org/widgets/invokeai/-/svg-badge.svg
-[translation status link]: https://hosted.weblate.org/engage/invokeai/
 
 </div>
 
-_**Note: This is an alpha release. Bugs are expected and not all
-features are fully implemented. Please use the GitHub [Issues
-pages](https://github.com/invoke-ai/InvokeAI/issues?q=is%3Aissue+is%3Aopen)
-to report unexpected problems. Also note that InvokeAI root directory
-which contains models, outputs and configuration files, has changed
-between the 2.x and 3.x release. If you wish to use your v2.3 root
-directory with v3.0, please follow the directions in [Migrating a 2.3
-root directory to 3.0](#migrating-to-3).**_
+InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.
 
-InvokeAI is a leading creative engine built to empower professionals
-and enthusiasts alike. Generate and create stunning visual media using
-the latest AI-driven technologies. InvokeAI offers an industry leading
-Web Interface, interactive Command Line Interface, and also serves as
-the foundation for multiple commercial products.
+**Quick links**: [[How to Install](#installation)] [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a href="https://invoke-ai.github.io/InvokeAI/">Documentation and Tutorials</a>] [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>] [<a href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion, Ideas & Q&A</a>]
 
-**Quick links**: [[How to
-  Install](https://invoke-ai.github.io/InvokeAI/#installation)] [<a
-  href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>] [<a
-  href="https://invoke-ai.github.io/InvokeAI/">Documentation and
-  Tutorials</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/">Code and
-  Downloads</a>] [<a
-  href="https://github.com/invoke-ai/InvokeAI/issues">Bug Reports</a>]
-  [<a
-  href="https://github.com/invoke-ai/InvokeAI/discussions">Discussion,
-  Ideas & Q&A</a>]
+_Note: InvokeAI is rapidly evolving. Please use the
+[Issues](https://github.com/invoke-ai/InvokeAI/issues) tab to report bugs and make feature
+requests. Be sure to use the provided templates. They will help us diagnose issues faster._
 
 <div align="center">
 
-![canvas preview](https://github.com/invoke-ai/InvokeAI/raw/main/docs/assets/canvas_preview.png)
+![canvas preview](https://github.com/mauwii/InvokeAI/raw/main/docs/assets/canvas_preview.png)
 
 </div>
 
-## Table of Contents
-
-Table of Contents 📝
-
-**Getting Started**
-1. 🏁 [Quick Start](#quick-start) 
-3. 🖥️ [Hardware Requirements](#hardware-requirements) 
-
-**More About Invoke**
-1. 🌟 [Features](#features) 
-2. 📣 [Latest Changes](#latest-changes) 
-3. 🛠️ [Troubleshooting](#troubleshooting) 
-
-**Supporting the Project**
-1. 🤝 [Contributing](#contributing) 
-2. 👥 [Contributors](#contributors) 
-3. 💕 [Support](#support) 
-
-## Quick Start
+# Getting Started with InvokeAI
 
 For full installation and upgrade instructions, please see:
 [InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/)
 
-If upgrading from version 2.3, please read [Migrating a 2.3 root
-directory to 3.0](#migrating-to-3) first.
-
-### Automatic Installer (suggested for 1st time users)
-
 1. Go to the bottom of the [Latest Release Page](https://github.com/invoke-ai/InvokeAI/releases/latest)
-
 2. Download the .zip file for your OS (Windows/macOS/Linux).
-
 3. Unzip the file.
+4. If you are on Windows, double-click on the `install.bat` script. On macOS, open a Terminal window, drag the file `install.sh` from Finder into the Terminal, and press return. On Linux, run `install.sh`.
+5. Wait a while, until it is done.
+6. The folder where you ran the installer from will now be filled with lots of files. If you are on Windows, double-click on the `invoke.bat` file. On macOS, open a Terminal window, drag `invoke.sh` from the folder into the Terminal, and press return. On Linux, run `invoke.sh`
+7. Press 2 to open the "browser-based UI", press enter/return, wait a minute or two for Stable Diffusion to start up, then open your browser and go to http://localhost:9090.
+8. Type `banana sushi` in the box on the top left and click `Invoke`
 
-4. **Windows:** double-click on the `install.bat` script. **macOS:** Open a Terminal window, drag the file `install.sh` from Finder
-into the Terminal, and press return. **Linux:** run `install.sh`.
 
-5. You'll be asked to confirm the location of the folder in which
-to install InvokeAI and its image generation model files. Pick a
-location with at least 15 GB of free memory. More if you plan on
-installing lots of models.
+## Table of Contents
 
-6. Wait while the installer does its thing. After installing the software,
-the installer will launch a script that lets you configure InvokeAI and
-select a set of starting image generation models.
+1. [Installation](#installation)
+2. [Hardware Requirements](#hardware-requirements)
+3. [Features](#features)
+4. [Latest Changes](#latest-changes)
+5. [Troubleshooting](#troubleshooting)
+6. [Contributing](#contributing)
+7. [Contributors](#contributors)
+8. [Support](#support)
+9. [Further Reading](#further-reading)
 
-7. Find the folder that InvokeAI was installed into (it is not the
-same as the unpacked zip file directory!) The default location of this
-folder (if you didn't change it in step 5) is `~/invokeai` on
-Linux/Mac systems, and `C:\Users\YourName\invokeai` on Windows. This directory will contain launcher scripts named `invoke.sh` and `invoke.bat`.
-
-8. On Windows systems, double-click on the `invoke.bat` file. On
-macOS, open a Terminal window, drag `invoke.sh` from the folder into
-the Terminal, and press return. On Linux, run `invoke.sh`
-
-9. Press 2 to open the "browser-based UI", press enter/return, wait a
-minute or two for Stable Diffusion to start up, then open your browser
-and go to http://localhost:9090.
-
-10. Type `banana sushi` in the box on the top left and click `Invoke`
-
-### Command-Line Installation (for developers and users familiar with Terminals)
-
-You must have Python 3.9 or 3.10 installed on your machine. Earlier or
-later versions are not supported.
-Node.js also needs to be installed along with yarn (can be installed with
-the command `npm install -g yarn` if needed)
-
-1. Open a command-line window on your machine. The PowerShell is recommended for Windows.
-2. Create a directory to install InvokeAI into. You'll need at least 15 GB of free space:
-
-    ```terminal
-    mkdir invokeai
-    ````
-
-3. Create a virtual environment named `.venv` inside this directory and activate it:
-
-    ```terminal
-    cd invokeai
-    python -m venv .venv --prompt InvokeAI
-    ```
-
-4. Activate the virtual environment (do it every time you run InvokeAI)
-
-    _For Linux/Mac users:_
-
-    ```sh
-    source .venv/bin/activate
-    ```
-
-    _For Windows users:_
-
-    ```ps
-    .venv\Scripts\activate
-    ```
-
-5. Install the InvokeAI module and its dependencies. Choose the command suited for your platform & GPU.
-
-    _For Windows/Linux with an NVIDIA GPU:_
-
-    ```terminal
-    pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
-    ```
-
-    _For Linux with an AMD GPU:_
-
-    ```sh
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
-    ```
-
-    _For non-GPU systems:_
-    ```terminal
-    pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-    ``` 
-
-    _For Macintoshes, either Intel or M1/M2:_
-
-    ```sh
-    pip install InvokeAI --use-pep517
-    ```
-
-6. Configure InvokeAI and install a starting set of image generation models (you only need to do this once):
-
-    ```terminal
-    invokeai-configure
-    ```
-
-7. Launch the web server (do it every time you run InvokeAI):
-
-    ```terminal
-    invokeai-web
-    ```
-
-8. Build Node.js assets
-
-  ```terminal
-  cd invokeai/frontend/web/
-  yarn vite build
-  ```
-
-9. Point your browser to http://localhost:9090 to bring up the web interface.
-10. Type `banana sushi` in the box on the top left and click `Invoke`.
-
-Be sure to activate the virtual environment each time before re-launching InvokeAI,
-using `source .venv/bin/activate` or `.venv\Scripts\activate`.
-
-## Detailed Installation Instructions
+## Installation
 
 This fork is supported across Linux, Windows and Macintosh. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
@@ -223,108 +80,30 @@ AMD card (using the ROCm driver). For full installation and upgrade
 instructions, please see:
 [InvokeAI Installation Overview](https://invoke-ai.github.io/InvokeAI/installation/INSTALL_SOURCE/)
 
-<a name="migrating-to-3"></a>
-### Migrating a v2.3 InvokeAI root directory
-
-The InvokeAI root directory is where the InvokeAI startup file,
-installed models, and generated images are stored. It is ordinarily
-named `invokeai` and located in your home directory. The contents and
-layout of this directory has changed between versions 2.3 and 3.0 and
-cannot be used directly.
-
-We currently recommend that you use the installer to create a new root
-directory named differently from the 2.3 one, e.g. `invokeai-3` and
-then use a migration script to copy your 2.3 models into the new
-location. However, if you choose, you can upgrade this directory in
-place.  This section gives both recipes.
-
-#### Creating a new root directory and migrating old models
-
-This is the safer recipe because it leaves your old root directory in
-place to fall back on.
-
-1. Follow the instructions above to create and install InvokeAI in a
-directory that has a different name from the 2.3 invokeai directory.
-In this example, we will use "invokeai-3"
-
-2. When you are prompted to select models to install, select a minimal
-set of models, such as stable-diffusion-v1.5 only.
-
-3. After installation is complete launch `invokeai.sh` (Linux/Mac) or
-`invokeai.bat` and select option 8 "Open the developers console". This
-will take you to the command line.
-
-4. Issue the command `invokeai-migrate3 --from /path/to/v2.3-root --to
-/path/to/invokeai-3-root`. Provide the correct `--from` and `--to`
-paths for your v2.3 and v3.0 root directories respectively.
-
-This will copy and convert your old models from 2.3 format to 3.0
-format and create a new `models` directory in the 3.0 directory. The
-old models directory (which contains the models selected at install
-time) will be renamed `models.orig` and can be deleted once you have
-confirmed that the migration was successful.
-
-#### Migrating in place
-
-For the adventurous, you may do an in-place upgrade from 2.3 to 3.0
-without touching the command line. The recipe is as follows>
-
-1. Launch the InvokeAI launcher script in your current v2.3 root directory.
-
-2. Select option [9] "Update InvokeAI" to bring up the updater dialog.
-
-3a. During the alpha release phase, select option [3] and manually
-enter the tag name `v3.0.0+a2`.
-
-3b. Once 3.0 is released, select option [1] to upgrade to the latest release.
-
-4. Once the upgrade is finished you will be returned to the launcher
-menu. Select option [7] "Re-run the configure script to fix a broken
-install or to complete a major upgrade".
-
-This will run the configure script against the v2.3 directory and
-update it to the 3.0 format. The following files will be replaced:
-
-  - The invokeai.init file, replaced by invokeai.yaml
-  - The models directory
-  - The configs/models.yaml model index
-  
-The original versions of these files will be saved with the suffix
-".orig" appended to the end. Once you have confirmed that the upgrade
-worked, you can safely remove these files. Alternatively you can
-restore a working v2.3 directory by removing the new files and
-restoring the ".orig" files' original names.
-
-#### Migration Caveats
-
-The migration script will migrate your invokeai settings and models,
-including textual inversion models, LoRAs and merges that you may have
-installed previously. However it does **not** migrate the generated
-images stored in your 2.3-format outputs directory. The released
-version of 3.0 is expected to have an interface for importing an
-entire directory of image files as a batch.
-
-## Hardware Requirements
+### Hardware Requirements
 
 InvokeAI is supported across Linux, Windows and macOS. Linux
 users can use either an Nvidia-based card (with CUDA support) or an
 AMD card (using the ROCm driver).
 
-### System
+#### System
 
 You will need one of the following:
 
 - An NVIDIA-based graphics card with 4 GB or more VRAM memory.
 - An Apple computer with an M1 chip.
-- An AMD-based graphics card with 4GB or more VRAM memory. (Linux only)
 
 We do not recommend the GTX 1650 or 1660 series video cards. They are
 unable to run in half-precision mode and do not have sufficient VRAM
 to render 512x512 images.
 
-**Memory** - At least 12 GB Main Memory RAM.
+#### Memory
 
-**Disk** - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
+- At least 12 GB Main Memory RAM.
+
+#### Disk
+
+- At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
 ## Features
 
@@ -338,24 +117,28 @@ InvokeAI offers a locally hosted Web Server & React Frontend, with an industry l
 
 The Unified Canvas is a fully integrated canvas implementation with support for all core generation capabilities, in/outpainting, brush tools, and more. This creative tool unlocks the capability for artists to create with AI as a creative collaborator, and can be used to augment AI-generated imagery, sketches, photography, renders, and more.
 
-### *Node Architecture & Editor (Beta)*
+### *Advanced Prompt Syntax*
 
-Invoke AI's backend is built on a graph-based execution architecture. This allows for customizable generation pipelines to be developed by professional users looking to create specific workflows to support their production use-cases, and will be extended in the future with additional capabilities.
+InvokeAI's advanced prompt syntax allows for token weighting, cross-attention control, and prompt blending, allowing for fine-tuned tweaking of your invocations and exploration of the latent space.
 
-### *Board & Gallery Management*
+### *Command Line Interface*
 
-Invoke AI provides an organized gallery system for easily storing, accessing, and remixing your content in the Invoke workspace. Images can be dragged/dropped onto any Image-base UI element in the application, and rich metadata within the Image allows for easy recall of key prompts or settings used in your workflow. 
+For users utilizing a terminal-based environment, or who want to take advantage of CLI features, InvokeAI offers an extensive and actively supported command-line interface that provides the full suite of generation functionality available in the tool.
 
 ### Other features
 
 - *Support for both ckpt and diffusers models*
 - *SD 2.0, 2.1 support*
-- *Upscaling Tools*
+- *Noise Control & Tresholding*
+- *Popular Sampler Support*
+- *Upscaling & Face Restoration Tools*
 - *Embedding Manager & Support*
 - *Model Manager & Support*
-- *Node-Based Architecture*
-- *Node-Based Plug-&-Play UI (Beta)*
-- *SDXL Support* (Coming soon)
+
+### Coming Soon
+
+- *Node-Based Architecture & UI*
+- And more...
 
 ### Latest Changes
 
@@ -363,20 +146,18 @@ For our latest changes, view our [Release
 Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
 [CHANGELOG](docs/CHANGELOG.md).
 
-### Troubleshooting
+## Troubleshooting
 
 Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
 problems and other issues.
 
-## Contributing
+# Contributing
 
 Anyone who wishes to contribute to this project, whether documentation, features, bug fixes, code
 cleanup, testing, or code reviews, is very much encouraged to do so.
 
 To join, just raise your hand on the InvokeAI Discord server (#dev-chat) or the GitHub discussion board.
 
-If you'd like to help with translation, please see our [translation guide](docs/other/TRANSLATION.md).
-
 If you are unfamiliar with how
 to contribute to GitHub projects, here is a
 [Getting Started Guide](https://opensource.com/article/19/7/create-pull-request-github). A full set of contribution guidelines, along with templates, are in progress. You can **make your pull request against the "main" branch**.

binary_installer/install.bat.in

@@ -0,0 +1,164 @@
+@echo off
+
+@rem This script will install git (if not found on the PATH variable)
+@rem  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+@rem For users who already have git, this step will be skipped.
+
+@rem Next, it'll download the project's source code.
+@rem Then it will download a self-contained, standalone Python and unpack it.
+@rem Finally, it'll create the Python virtual environment and preload the models.
+
+@rem This enables a user to install this project without manually installing git or Python
+
+@rem change to the script's directory
+PUSHD "%~dp0"
+
+set "no_cache_dir=--no-cache-dir"
+if "%1" == "use-cache" (
+    set "no_cache_dir="
+)
+
+echo ***** Installing InvokeAI.. *****
+@rem Config
+set INSTALL_ENV_DIR=%cd%\installer_files\env
+@rem https://mamba.readthedocs.io/en/latest/installation.html
+set MICROMAMBA_DOWNLOAD_URL=https://github.com/cmdr2/stable-diffusion-ui/releases/download/v1.1/micromamba.exe
+set RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+set RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+set PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+set PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-x86_64-pc-windows-msvc-shared-install_only.tar.gz
+
+set PACKAGES_TO_INSTALL=
+
+call git --version >.tmp1 2>.tmp2
+if "%ERRORLEVEL%" NEQ "0" set PACKAGES_TO_INSTALL=%PACKAGES_TO_INSTALL% git
+
+@rem Cleanup
+del /q .tmp1 .tmp2
+
+@rem (if necessary) install git into a contained environment
+if "%PACKAGES_TO_INSTALL%" NEQ "" (
+    @rem download micromamba
+    echo ***** Downloading micromamba from %MICROMAMBA_DOWNLOAD_URL% to micromamba.exe *****
+
+    call curl -L "%MICROMAMBA_DOWNLOAD_URL%" > micromamba.exe
+
+    @rem test the mamba binary
+    echo ***** Micromamba version:  *****
+    call micromamba.exe --version
+
+    @rem create the installer env
+    if not exist "%INSTALL_ENV_DIR%" (
+        call micromamba.exe create -y --prefix "%INSTALL_ENV_DIR%"
+    )
+
+    echo ***** Packages to install:%PACKAGES_TO_INSTALL% *****
+
+    call micromamba.exe install -y --prefix "%INSTALL_ENV_DIR%" -c conda-forge %PACKAGES_TO_INSTALL%
+
+    if not exist "%INSTALL_ENV_DIR%" (
+        echo ----- There was a problem while installing "%PACKAGES_TO_INSTALL%" using micromamba. Cannot continue. -----
+        pause
+        exit /b
+    )
+)
+
+del /q micromamba.exe
+
+@rem For 'git' only
+set PATH=%INSTALL_ENV_DIR%\Library\bin;%PATH%
+
+@rem Download/unpack/clean up InvokeAI release sourceball
+set err_msg=----- InvokeAI source download failed -----
+echo Trying to download "%RELEASE_URL%%RELEASE_SOURCEBALL%"
+curl -L %RELEASE_URL%%RELEASE_SOURCEBALL% --output InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- InvokeAI source unpack failed -----
+tar -zxf InvokeAI.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q InvokeAI.tgz
+
+set err_msg=----- InvokeAI source copy failed -----
+cd InvokeAI-*
+xcopy . .. /e /h
+if %errorlevel% neq 0 goto err_exit
+cd ..
+
+@rem cleanup
+for /f %%i in ('dir /b InvokeAI-*') do rd /s /q %%i
+rd /s /q .dev_scripts .github docker-build tests
+del /q requirements.in requirements-mkdocs.txt shell.nix
+
+echo ***** Unpacked InvokeAI source *****
+
+@rem Download/unpack/clean up python-build-standalone
+set err_msg=----- Python download failed -----
+curl -L %PYTHON_BUILD_STANDALONE_URL%/%PYTHON_BUILD_STANDALONE% --output python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- Python unpack failed -----
+tar -zxf python.tgz
+if %errorlevel% neq 0 goto err_exit
+
+del /q python.tgz
+
+echo ***** Unpacked python-build-standalone *****
+
+@rem create venv
+set err_msg=----- problem creating venv -----
+.\python\python -E -s -m venv .venv
+if %errorlevel% neq 0 goto err_exit
+call .venv\Scripts\activate.bat
+
+echo ***** Created Python virtual environment *****
+
+@rem Print venv's Python version
+set err_msg=----- problem calling venv's python -----
+echo We're running under
+.venv\Scripts\python --version
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- pip update failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location --upgrade pip wheel
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Updated pip and wheel *****
+
+set err_msg=----- requirements file copy failed -----
+copy binary_installer\py3.10-windows-x86_64-cuda-reqs.txt requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+set err_msg=----- main pip install failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -r requirements.txt
+if %errorlevel% neq 0 goto err_exit
+
+echo ***** Installed Python dependencies *****
+
+set err_msg=----- InvokeAI setup failed -----
+.venv\Scripts\python -m pip install %no_cache_dir% --no-warn-script-location -e .
+if %errorlevel% neq 0 goto err_exit
+
+copy binary_installer\invoke.bat.in .\invoke.bat
+echo ***** Installed invoke launcher script ******
+
+@rem more cleanup
+rd /s /q binary_installer installer_files
+
+@rem preload the models
+call .venv\Scripts\python scripts\configure_invokeai.py
+set err_msg=----- model download clone failed -----
+if %errorlevel% neq 0 goto err_exit
+deactivate
+
+echo ***** Finished downloading models *****
+
+echo All done! Execute the file invoke.bat in this directory to start InvokeAI
+pause
+exit
+
+:err_exit
+    echo %err_msg%
+    pause
+    exit

binary_installer/install.sh.in

@@ -0,0 +1,235 @@
+#!/usr/bin/env bash
+
+# ensure we're in the correct folder in case user's CWD is somewhere else
+scriptdir=$(dirname "$0")
+cd "$scriptdir"
+
+set -euo pipefail
+IFS=$'\n\t'
+
+function _err_exit {
+    if test "$1" -ne 0
+    then
+        echo -e "Error code $1; Error caught was '$2'"
+        read -p "Press any key to exit..."
+        exit
+    fi
+}
+
+# This script will install git (if not found on the PATH variable)
+#  using micromamba (an 8mb static-linked single-file binary, conda replacement).
+# For users who already have git, this step will be skipped.
+
+# Next, it'll download the project's source code.
+# Then it will download a self-contained, standalone Python and unpack it.
+# Finally, it'll create the Python virtual environment and preload the models.
+
+# This enables a user to install this project without manually installing git or Python
+
+echo -e "\n***** Installing InvokeAI into $(pwd)... *****\n"
+
+export no_cache_dir="--no-cache-dir"
+if [ $# -ge 1 ]; then
+    if [ "$1" = "use-cache" ]; then
+        export no_cache_dir=""
+    fi
+fi
+
+
+OS_NAME=$(uname -s)
+case "${OS_NAME}" in
+    Linux*)     OS_NAME="linux";;
+    Darwin*)    OS_NAME="darwin";;
+    *)          echo -e "\n----- Unknown OS: $OS_NAME! This script runs only on Linux or macOS -----\n" && exit
+esac
+
+OS_ARCH=$(uname -m)
+case "${OS_ARCH}" in
+    x86_64*)    ;;
+    arm64*)     ;;
+    *)          echo -e "\n----- Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64 -----\n" && exit
+esac
+
+# https://mamba.readthedocs.io/en/latest/installation.html
+MAMBA_OS_NAME=$OS_NAME
+MAMBA_ARCH=$OS_ARCH
+if [ "$OS_NAME" == "darwin" ]; then
+    MAMBA_OS_NAME="osx"
+fi
+
+if [ "$OS_ARCH" == "linux" ]; then
+    MAMBA_ARCH="aarch64"
+fi
+
+if [ "$OS_ARCH" == "x86_64" ]; then
+    MAMBA_ARCH="64"
+fi
+
+PY_ARCH=$OS_ARCH
+if [ "$OS_ARCH" == "arm64" ]; then
+    PY_ARCH="aarch64"
+fi
+
+# Compute device ('cd' segment of reqs files) detect goes here
+# This needs a ton of work
+# Suggestions:
+#   - lspci
+#   - check $PATH for nvidia-smi, gtt CUDA/GPU version from output
+#   - Surely there's a similar utility for AMD?
+CD="cuda"
+if [ "$OS_NAME" == "darwin" ] && [ "$OS_ARCH" == "arm64" ]; then
+    CD="mps"
+fi
+
+# config
+INSTALL_ENV_DIR="$(pwd)/installer_files/env"
+MICROMAMBA_DOWNLOAD_URL="https://micro.mamba.pm/api/micromamba/${MAMBA_OS_NAME}-${MAMBA_ARCH}/latest"
+RELEASE_URL=https://github.com/invoke-ai/InvokeAI
+RELEASE_SOURCEBALL=/archive/refs/heads/main.tar.gz
+PYTHON_BUILD_STANDALONE_URL=https://github.com/indygreg/python-build-standalone/releases/download
+if [ "$OS_NAME" == "darwin" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-apple-darwin-install_only.tar.gz
+elif [ "$OS_NAME" == "linux" ]; then
+    PYTHON_BUILD_STANDALONE=20221002/cpython-3.10.7+20221002-${PY_ARCH}-unknown-linux-gnu-install_only.tar.gz
+fi
+echo "INSTALLING $RELEASE_SOURCEBALL FROM $RELEASE_URL"
+
+PACKAGES_TO_INSTALL=""
+
+if ! hash "git" &>/dev/null; then PACKAGES_TO_INSTALL="$PACKAGES_TO_INSTALL git"; fi
+
+# (if necessary) install git and conda into a contained environment
+if [ "$PACKAGES_TO_INSTALL" != "" ]; then
+    # download micromamba
+    echo -e "\n***** Downloading micromamba from $MICROMAMBA_DOWNLOAD_URL to micromamba *****\n"
+
+    curl -L "$MICROMAMBA_DOWNLOAD_URL" | tar -xvjO bin/micromamba > micromamba
+
+    chmod u+x ./micromamba
+
+    # test the mamba binary
+    echo -e "\n***** Micromamba version: *****\n"
+    ./micromamba --version
+
+    # create the installer env
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        ./micromamba create -y --prefix "$INSTALL_ENV_DIR"
+    fi
+
+    echo -e "\n***** Packages to install:$PACKAGES_TO_INSTALL *****\n"
+
+    ./micromamba install -y --prefix "$INSTALL_ENV_DIR" -c conda-forge "$PACKAGES_TO_INSTALL"
+
+    if [ ! -e "$INSTALL_ENV_DIR" ]; then
+        echo -e "\n----- There was a problem while initializing micromamba. Cannot continue. -----\n"
+        exit
+    fi
+fi
+
+rm -f micromamba.exe
+
+export PATH="$INSTALL_ENV_DIR/bin:$PATH"
+
+# Download/unpack/clean up InvokeAI release sourceball
+_err_msg="\n----- InvokeAI source download failed -----\n"
+curl -L $RELEASE_URL/$RELEASE_SOURCEBALL --output InvokeAI.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- InvokeAI source unpack failed -----\n"
+tar -zxf InvokeAI.tgz
+_err_exit $? _err_msg
+
+rm -f InvokeAI.tgz
+
+_err_msg="\n----- InvokeAI source copy failed -----\n"
+cd InvokeAI-*
+cp -r . ..
+_err_exit $? _err_msg
+cd ..
+
+# cleanup
+rm -rf InvokeAI-*/
+rm -rf .dev_scripts/ .github/ docker-build/ tests/ requirements.in requirements-mkdocs.txt shell.nix
+
+echo -e "\n***** Unpacked InvokeAI source *****\n"
+
+# Download/unpack/clean up python-build-standalone
+_err_msg="\n----- Python download failed -----\n"
+curl -L $PYTHON_BUILD_STANDALONE_URL/$PYTHON_BUILD_STANDALONE --output python.tgz
+_err_exit $? _err_msg
+_err_msg="\n----- Python unpack failed -----\n"
+tar -zxf python.tgz
+_err_exit $? _err_msg
+
+rm -f python.tgz
+
+echo -e "\n***** Unpacked python-build-standalone *****\n"
+
+# create venv
+_err_msg="\n----- problem creating venv -----\n"
+
+if [ "$OS_NAME" == "darwin" ]; then
+    # patch sysconfig so that extensions can build properly
+    # adapted from https://github.com/cashapp/hermit-packages/commit/fcba384663892f4d9cfb35e8639ff7a28166ee43
+    PYTHON_INSTALL_DIR="$(pwd)/python"
+    SYSCONFIG="$(echo python/lib/python*/_sysconfigdata_*.py)"
+    TMPFILE="$(mktemp)"
+    chmod +w "${SYSCONFIG}"
+    cp "${SYSCONFIG}" "${TMPFILE}"
+    sed "s,'/install,'${PYTHON_INSTALL_DIR},g" "${TMPFILE}" > "${SYSCONFIG}"
+    rm -f "${TMPFILE}"
+fi
+
+./python/bin/python3 -E -s -m venv .venv
+_err_exit $? _err_msg
+source .venv/bin/activate
+
+echo -e "\n***** Created Python virtual environment *****\n"
+
+# Print venv's Python version
+_err_msg="\n----- problem calling venv's python -----\n"
+echo -e "We're running under"
+.venv/bin/python3 --version
+_err_exit $? _err_msg
+
+_err_msg="\n----- pip update failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location --upgrade pip
+_err_exit $? _err_msg
+
+echo -e "\n***** Updated pip *****\n"
+
+_err_msg="\n----- requirements file copy failed -----\n"
+cp binary_installer/py3.10-${OS_NAME}-"${OS_ARCH}"-${CD}-reqs.txt requirements.txt
+_err_exit $? _err_msg
+
+_err_msg="\n----- main pip install failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -r requirements.txt
+_err_exit $? _err_msg
+
+echo -e "\n***** Installed Python dependencies *****\n"
+
+_err_msg="\n----- InvokeAI setup failed -----\n"
+.venv/bin/python3 -m pip install $no_cache_dir --no-warn-script-location -e .
+_err_exit $? _err_msg
+
+echo -e "\n***** Installed InvokeAI *****\n"
+
+cp binary_installer/invoke.sh.in ./invoke.sh
+chmod a+rx ./invoke.sh
+echo -e "\n***** Installed invoke launcher script ******\n"
+
+# more cleanup
+rm -rf binary_installer/ installer_files/
+
+# preload the models
+.venv/bin/python3 scripts/configure_invokeai.py
+_err_msg="\n----- model download clone failed -----\n"
+_err_exit $? _err_msg
+deactivate
+
+echo -e "\n***** Finished downloading models *****\n"
+
+echo "All done! Run the command"
+echo "  $scriptdir/invoke.sh"
+echo "to start InvokeAI."
+read -p "Press any key to exit..."
+exit

binary_installer/invoke.bat.in

@@ -0,0 +1,36 @@
+@echo off
+
+PUSHD "%~dp0"
+call .venv\Scripts\activate.bat
+
+echo Do you want to generate images using the
+echo 1. command-line
+echo 2. browser-based UI
+echo OR
+echo 3. open the developer console
+set /p choice="Please enter 1, 2 or 3: "
+if /i "%choice%" == "1" (
+    echo Starting the InvokeAI command-line.
+    .venv\Scripts\python scripts\invoke.py %*
+) else if /i "%choice%" == "2" (
+    echo Starting the InvokeAI browser-based UI.
+    .venv\Scripts\python scripts\invoke.py --web %*
+) else if /i "%choice%" == "3" (
+    echo Developer Console
+    echo Python command is:
+    where python
+    echo Python version is:
+    python --version
+    echo *************************
+    echo You are now in the system shell, with the local InvokeAI Python virtual environment activated,
+    echo so that you can troubleshoot this InvokeAI installation as necessary.
+    echo *************************
+    echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
+    call cmd /k
+) else (
+    echo Invalid selection
+    pause
+    exit /b
+)
+
+deactivate

binary_installer/invoke.sh.in

@@ -0,0 +1,46 @@
+#!/usr/bin/env sh
+
+set -eu
+
+. .venv/bin/activate
+
+# set required env var for torch on mac MPS
+ if [ "$(uname -s)" == "Darwin" ]; then
+     export PYTORCH_ENABLE_MPS_FALLBACK=1
+ fi
+
+echo "Do you want to generate images using the"
+echo "1. command-line"
+echo "2. browser-based UI"
+echo "OR"
+echo "3. open the developer console"
+echo "Please enter 1, 2, or 3:"
+read choice
+
+case $choice in
+    1)
+        printf "\nStarting the InvokeAI command-line..\n";
+        .venv/bin/python scripts/invoke.py $*;
+    ;;
+    2)
+        printf "\nStarting the InvokeAI browser-based UI..\n";
+        .venv/bin/python scripts/invoke.py --web $*;
+    ;;
+    3)
+        printf "\nDeveloper Console:\n";
+        printf "Python command is:\n\t";
+        which python;
+        printf "Python version is:\n\t";
+        python --version;
+        echo "*************************"
+        echo "You are now in your user shell ($SHELL) with the local InvokeAI Python virtual environment activated,";
+        echo "so that you can troubleshoot this InvokeAI installation as necessary.";
+        printf "*************************\n"
+        echo "*** Type \`exit\` to quit this shell and deactivate the Python virtual environment *** ";
+        /usr/bin/env "$SHELL";
+    ;;
+    *)
+        echo "Invalid selection";
+        exit
+    ;;
+esac

binary_installer/readme.txt

@@ -0,0 +1,17 @@
+InvokeAI
+
+Project homepage: https://github.com/invoke-ai/InvokeAI
+
+Installation on Windows:
+    NOTE: You might need to enable Windows Long Paths. If you're not sure,
+    then you almost certainly need to. Simply double-click the 'WinLongPathsEnabled.reg'
+    file. Note that you will need to have admin privileges in order to
+    do this.
+
+    Please double-click the 'install.bat' file (while keeping it inside the invokeAI folder).
+
+Installation on Linux and Mac:
+    Please open the terminal, and run './install.sh' (while keeping it inside the invokeAI folder).
+
+After installation, please run the 'invoke.bat' file (on Windows) or 'invoke.sh'
+file (on Linux/Mac) to start InvokeAI.

binary_installer/requirements.in

@@ -0,0 +1,33 @@
+--prefer-binary
+--extra-index-url https://download.pytorch.org/whl/torch_stable.html
+--extra-index-url https://download.pytorch.org/whl/cu116
+--trusted-host https://download.pytorch.org
+accelerate~=0.15
+albumentations
+diffusers[torch]~=0.11
+einops
+eventlet
+flask_cors
+flask_socketio
+flaskwebgui==1.0.3
+getpass_asterisk
+imageio-ffmpeg
+pyreadline3
+realesrgan
+send2trash
+streamlit
+taming-transformers-rom1504
+test-tube
+torch-fidelity
+torch==1.12.1 ; platform_system == 'Darwin'
+torch==1.12.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
+torchvision==0.13.1 ; platform_system == 'Darwin'
+torchvision==0.13.0+cu116 ; platform_system == 'Linux' or platform_system == 'Windows'
+transformers
+picklescan
+https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip
+https://github.com/invoke-ai/clipseg/archive/1f754751c85d7d4255fa681f4491ff5711c1c288.zip
+https://github.com/invoke-ai/GFPGAN/archive/3f5d2397361199bc4a91c08bb7d80f04d7805615.zip ; platform_system=='Windows'
+https://github.com/invoke-ai/GFPGAN/archive/c796277a1cf77954e5fc0b288d7062d162894248.zip ; platform_system=='Linux' or platform_system=='Darwin'
+https://github.com/Birch-san/k-diffusion/archive/363386981fee88620709cf8f6f2eea167bd6cd74.zip
+https://github.com/invoke-ai/PyPatchMatch/archive/129863937a8ab37f6bbcec327c994c0f932abdbc.zip

coverage/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore

docker/.env.sample

@@ -1,13 +0,0 @@
-## Make a copy of this file named `.env` and fill in the values below.
-## Any environment variables supported by InvokeAI can be specified here.
-
-# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
-# Outputs will also be stored here by default.
-# This **must** be an absolute path.
-INVOKEAI_ROOT=
-
-HUGGINGFACE_TOKEN=
-
-## optional variables specific to the docker setup
-# GPU_DRIVER=cuda
-# CONTAINER_UID=1000

docker/Dockerfile

@@ -1,129 +1,112 @@
-# syntax=docker/dockerfile:1.4
+# syntax=docker/dockerfile:1
 
-## Builder stage
+# Maintained by Matthias Wild <mauwii@outlook.de>
 
-FROM library/ubuntu:22.04 AS builder
+ARG PYTHON_VERSION=3.9
+##################
+### base image ###
+##################
+FROM python:${PYTHON_VERSION}-slim AS python-base
 
-ARG DEBIAN_FRONTEND=noninteractive
-RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt update && apt-get install -y \
-        git \
-        python3.10-venv \
-        python3-pip \
-        build-essential
+# Install necesarry packages
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    libgl1-mesa-glx=20.3.* \
+    libglib2.0-0=2.66.* \
+    libopencv-dev=4.5.* \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
+# set working directory and path
+ARG APPDIR=/usr/src
+ARG APPNAME=InvokeAI
+WORKDIR ${APPDIR}
+ENV PATH=${APPDIR}/${APPNAME}/bin:$PATH
 
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-ARG TORCH_VERSION=2.0.1
-ARG TORCHVISION_VERSION=0.15.2
-ARG GPU_DRIVER=cuda
-ARG TARGETPLATFORM="linux/amd64"
-# unused but available
-ARG BUILDPLATFORM
+######################
+### build frontend ###
+######################
+FROM node:lts as frontend-builder
 
-WORKDIR ${INVOKEAI_SRC}
+# Copy Sources
+ARG APPDIR=/usr/src
+WORKDIR ${APPDIR}
+COPY --link . .
 
-# Install pytorch before all other pip packages
-# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
-# x86_64/CUDA is default
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m venv ${VIRTUAL_ENV} &&\
-    if [ "$TARGETPLATFORM" = "linux/arm64" ] || [ "$GPU_DRIVER" = "cpu" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cpu"; \
-    elif [ "$GPU_DRIVER" = "rocm" ]; then \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
-    else \
-        extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
-    fi &&\
-    pip install $extra_index_url_arg \
-        torch==$TORCH_VERSION \
-        torchvision==$TORCHVISION_VERSION
+# install dependencies and build frontend
+WORKDIR ${APPDIR}/invokeai/frontend
+RUN \
+  --mount=type=cache,target=/usr/local/share/.cache/yarn/v6 \
+  yarn install \
+  --prefer-offline \
+  --frozen-lockfile \
+  --non-interactive \
+  --production=false \
+  && yarn build
 
-# Install the local package.
-# Editable mode helps use the same image for development:
-# the local working copy can be bind-mounted into the image
-# at path defined by ${INVOKEAI_SRC}
-COPY invokeai ./invokeai
-COPY pyproject.toml ./
-RUN --mount=type=cache,target=/root/.cache/pip \
-    # xformers + triton fails to install on arm64
-    if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
-        pip install -e ".[xformers]"; \
-    else \
-        pip install -e "."; \
-    fi
+###################################
+### install python dependencies ###
+###################################
+FROM python-base AS pyproject-builder
 
-# #### Build the Web UI ------------------------------------
+# Install dependencies
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    gcc=4:10.2.* \
+    python3-dev=3.9.* \
+  && apt-get clean \
+  && rm -rf /var/lib/apt/lists/*
 
-FROM node:18 AS web-builder
-WORKDIR /build
-COPY invokeai/frontend/web/ ./
-RUN --mount=type=cache,target=/usr/lib/node_modules \
-    npm install --include dev
-RUN --mount=type=cache,target=/usr/lib/node_modules \
-    yarn vite build
+# create virtual environment
+RUN python3 -m venv "${APPNAME}" \
+  --upgrade-deps
 
+# copy sources
+COPY --from=frontend-builder ${APPDIR} .
 
-#### Runtime stage ---------------------------------------
+# install pyproject.toml
+ARG PIP_EXTRA_INDEX_URL
+ENV PIP_EXTRA_INDEX_URL ${PIP_EXTRA_INDEX_URL}
+RUN --mount=type=cache,target=/root/.cache/pip,sharing=locked \
+  "${APPDIR}/${APPNAME}/bin/pip" install \
+  --use-pep517 \
+  .
 
-FROM library/ubuntu:22.04 AS runtime
+#####################
+### runtime image ###
+#####################
+FROM python-base AS runtime
 
-ARG DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
-
-RUN apt update && apt install -y --no-install-recommends \
-        git \
-        curl \
-        vim \
-        tmux \
-        ncdu \
-        iotop \
-        bzip2 \
-        gosu \
-        libglib2.0-0 \
-        libgl1-mesa-glx \
-        python3-venv \
-        python3-pip \
-        build-essential \
-        libopencv-dev \
-        libstdc++-10-dev &&\
-    apt-get clean && apt-get autoclean
-
-# globally add magic-wormhole
-# for ease of transferring data to and from the container
-# when running in sandboxed cloud environments; e.g. Runpod etc.
-RUN pip install magic-wormhole
-
-ENV INVOKEAI_SRC=/opt/invokeai
-ENV VIRTUAL_ENV=/opt/venv/invokeai
-ENV INVOKEAI_ROOT=/invokeai
-ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
-
-# --link requires buldkit w/ dockerfile syntax 1.4
-COPY --link --from=builder ${INVOKEAI_SRC} ${INVOKEAI_SRC}
-COPY --link --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
-COPY --link --from=web-builder /build/dist ${INVOKEAI_SRC}/invokeai/frontend/web/dist
-
-# Link amdgpu.ids for ROCm builds
-# contributed by https://github.com/Rubonnek
-RUN mkdir -p "/opt/amdgpu/share/libdrm" &&\
-  ln -s "/usr/share/libdrm/amdgpu.ids" "/opt/amdgpu/share/libdrm/amdgpu.ids"
-
-WORKDIR ${INVOKEAI_SRC}
+# setup environment
+COPY --from=pyproject-builder ${APPDIR}/${APPNAME} ${APPDIR}/${APPNAME}
+ENV INVOKEAI_ROOT=/data
+ENV INVOKE_MODEL_RECONFIGURE="--yes --default_only"
 
 # build patchmatch
-RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
-RUN python3 -c "from patchmatch import patch_match"
+RUN \
+  --mount=type=cache,target=/var/cache/apt,sharing=locked \
+  --mount=type=cache,target=/var/lib/apt,sharing=locked \
+  apt-get update \
+  && apt-get install -y \
+    --no-install-recommends \
+    build-essential=12.9 \
+  && PYTHONDONTWRITEBYTECODE=1 \
+    python3 -c "from patchmatch import patch_match" \
+  && apt-get remove -y \
+    --autoremove \
+    build-essential \
+  && apt-get autoclean \
+  && rm -rf /var/lib/apt/lists/*
 
-# Create unprivileged user and make the local dir
-RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
-RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
-
-COPY docker/docker-entrypoint.sh ./
-ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
-CMD ["invokeai-web", "--host", "0.0.0.0"]
+# set Entrypoint and default CMD
+ENTRYPOINT [ "invokeai" ]
+CMD [ "--web", "--host=0.0.0.0" ]
+VOLUME [ "/data" ]

docker/README.md

@@ -1,77 +0,0 @@
-# InvokeAI Containerized
-
-All commands are to be run from the `docker` directory: `cd docker`
-
-#### Linux
-
-1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
-2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
-    - The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
-3. Ensure docker daemon is able to access the GPU.
-    - You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
-
-#### macOS
-
-1. Ensure Docker has at least 16GB RAM
-2. Enable VirtioFS for file sharing
-3. Enable `docker compose` V2 support
-
-This is done via Docker Desktop preferences
-
-## Quickstart
-
-
-1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
-    a. the desired location of the InvokeAI runtime directory, or
-    b. an existing, v3.0.0 compatible runtime directory.
-1. `docker compose up`
-
-The image will be built automatically if needed.
-
-The runtime directory (holding models and outputs) will be created in the location specified by `INVOKEAI_ROOT`. The default location is `~/invokeai`. The runtime directory will be populated with the base configs and models necessary to start generating.
-
-### Use a GPU
-
-- Linux is *recommended* for GPU support in Docker.
-- WSL2 is *required* for Windows.
-- only `x86_64` architecture is supported.
-
-The Docker daemon on the system must be already set up to use the GPU. In case of Linux, this involves installing `nvidia-docker-runtime` and configuring the `nvidia` runtime as default. Steps will be different for AMD. Please see Docker documentation for the most up-to-date instructions for using your GPU with Docker.
-
-## Customize
-
-Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
-
-You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
-
-Example (most values are optional):
-
-```
-INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
-HUGGINGFACE_TOKEN=the_actual_token
-CONTAINER_UID=1000
-GPU_DRIVER=cuda
-```
-
-## Even Moar Customizing!
-
-See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
-
-### Reconfigure the runtime directory
-
-Can be used to download additional models from the supported model list
-
-In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
-
-```
-command:
-  - invokeai-configure
-  - --yes
-```
-
-Or install models:
-
-```
-command:
-  - invokeai-model-install
-```

docker/build.sh

@@ -1,11 +1,43 @@
 #!/usr/bin/env bash
 set -e
 
-build_args=""
+# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#setup
+# Some possible pip extra-index urls (cuda 11.7 is available without extra url):
+#   CUDA 11.6:  https://download.pytorch.org/whl/cu116
+#   ROCm 5.2:   https://download.pytorch.org/whl/rocm5.2
+#   CPU:        https://download.pytorch.org/whl/cpu
+#   as found on https://pytorch.org/get-started/locally/
 
-[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
+SCRIPTDIR=$(dirname "$0")
+cd "$SCRIPTDIR" || exit 1
 
-echo "docker-compose build args:"
-echo $build_args
+source ./env.sh
 
-docker-compose build $build_args
+DOCKERFILE=${INVOKE_DOCKERFILE:-Dockerfile}
+
+# print the settings
+echo -e "You are using these values:\n"
+echo -e "Dockerfile: \t${DOCKERFILE}"
+echo -e "index-url: \t${PIP_EXTRA_INDEX_URL:-none}"
+echo -e "Volumename: \t${VOLUMENAME}"
+echo -e "Platform: \t${PLATFORM}"
+echo -e "Registry: \t${CONTAINER_REGISTRY}"
+echo -e "Repository: \t${CONTAINER_REPOSITORY}"
+echo -e "Container Tag: \t${CONTAINER_TAG}"
+echo -e "Container Image: ${CONTAINER_IMAGE}\n"
+
+# Create docker volume
+if [[ -n "$(docker volume ls -f name="${VOLUMENAME}" -q)" ]]; then
+    echo -e "Volume already exists\n"
+else
+    echo -n "createing docker volume "
+    docker volume create "${VOLUMENAME}"
+fi
+
+# Build Container
+docker build \
+    --platform="${PLATFORM}" \
+    --tag="${CONTAINER_IMAGE}" \
+    ${PIP_EXTRA_INDEX_URL:+--build-arg="PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}"} \
+    --file="${DOCKERFILE}" \
+    ..

docker/docker-compose.yml

@@ -1,48 +0,0 @@
-# Copyright (c) 2023 Eugene Brodsky https://github.com/ebr
-
-version: '3.8'
-
-services:
-  invokeai:
-    image: "local/invokeai:latest"
-    # edit below to run on a container runtime other than nvidia-container-runtime.
-    # not yet tested with rocm/AMD GPUs
-    # Comment out the "deploy" section to run on CPU only
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
-    build:
-      context: ..
-      dockerfile: docker/Dockerfile
-
-    # variables without a default will automatically inherit from the host environment
-    environment:
-      - INVOKEAI_ROOT
-      - HF_HOME
-
-    # Create a .env file in the same directory as this docker-compose.yml file
-    # and populate it with environment variables. See .env.sample
-    env_file:
-      - .env
-
-    ports:
-      - "${INVOKEAI_PORT:-9090}:9090"
-    volumes:
-      - ${INVOKEAI_ROOT:-~/invokeai}:${INVOKEAI_ROOT:-/invokeai}
-      - ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
-      # - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
-      # - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
-    tty: true
-    stdin_open: true
-
-    # # Example of running alternative commands/scripts in the container
-    # command:
-    #   - bash
-    #   - -c
-    #   - |
-    #     invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
-    #     invokeai-nodes-web --host 0.0.0.0

docker/docker-entrypoint.sh

@@ -1,65 +0,0 @@
-#!/bin/bash
-set -e -o pipefail
-
-### Container entrypoint
-# Runs the CMD as defined by the Dockerfile or passed to `docker run`
-# Can be used to configure the runtime dir
-# Bypass by using ENTRYPOINT or `--entrypoint`
-
-### Set INVOKEAI_ROOT pointing to a valid runtime directory
-# Otherwise configure the runtime dir first.
-
-### Configure the InvokeAI runtime directory (done by default)):
-# docker run --rm -it <this image> --configure
-# or skip with --no-configure
-
-### Set the CONTAINER_UID envvar to match your user.
-# Ensures files created in the container are owned by you:
-#   docker run --rm -it -v /some/path:/invokeai -e CONTAINER_UID=$(id -u) <this image>
-# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
-
-USER_ID=${CONTAINER_UID:-1000}
-USER=invoke
-usermod -u ${USER_ID} ${USER} 1>/dev/null
-
-configure() {
-    # Configure the runtime directory
-    if [[ -f ${INVOKEAI_ROOT}/invokeai.yaml ]]; then
-        echo "${INVOKEAI_ROOT}/invokeai.yaml exists. InvokeAI is already configured."
-        echo "To reconfigure InvokeAI, delete the above file."
-        echo "======================================================================"
-    else
-        mkdir -p ${INVOKEAI_ROOT}
-        chown --recursive ${USER} ${INVOKEAI_ROOT}
-        gosu ${USER} invokeai-configure --yes --default_only
-    fi
-}
-
-## Skip attempting to configure.
-## Must be passed first, before any other args.
-if [[ $1 != "--no-configure" ]]; then
-    configure
-else
-    shift
-fi
-
-### Set the $PUBLIC_KEY env var to enable SSH access.
-# We do not install openssh-server in the image by default to avoid bloat.
-# but it is useful to have the full SSH server e.g. on Runpod.
-# (use SCP to copy files to/from the image, etc)
-if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
-    apt-get update
-    apt-get install -y openssh-server
-    pushd $HOME
-    mkdir -p .ssh
-    echo ${PUBLIC_KEY} > .ssh/authorized_keys
-    chmod -R 700 .ssh
-    popd
-    service ssh start
-fi
-
-
-cd ${INVOKEAI_ROOT}
-
-# Run the CMD as the Container User (not root).
-exec gosu ${USER} "$@"

docker/env.sh

@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+
+if [[ -z "$PIP_EXTRA_INDEX_URL" ]]; then
+  # Decide which container flavor to build if not specified
+  if [[ -z "$CONTAINER_FLAVOR" ]]; then
+    # Check for CUDA and ROCm
+    CUDA_AVAILABLE=$(python -c "import torch;print(torch.cuda.is_available())")
+    ROCM_AVAILABLE=$(python -c "import torch;print(torch.version.hip is not None)")
+    if  [[ "$(uname -s)" != "Darwin" && "${CUDA_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR=cuda
+    elif [[ "$(uname -s)" != "Darwin" && "${ROCM_AVAILABLE}" == "True" ]]; then
+      CONTAINER_FLAVOR="rocm"
+    else
+      CONTAINER_FLAVOR="cpu"
+    fi
+  fi
+  # Set PIP_EXTRA_INDEX_URL based on container flavor
+  if [[ "$CONTAINER_FLAVOR" == "rocm" ]]; then
+    PIP_EXTRA_INDEX_URL="${PIP_EXTRA_INDEX_URL-"https://download.pytorch.org/whl/rocm"}"
+  elif CONTAINER_FLAVOR=cpu; then
+    PIP_EXTRA_INDEX_URL="${PIP_EXTRA_INDEX_URL-"https://download.pytorch.org/whl/cpu"}"
+  fi
+fi
+
+# Variables shared by build.sh and run.sh
+REPOSITORY_NAME="${REPOSITORY_NAME-$(basename "$(git rev-parse --show-toplevel)")}"
+VOLUMENAME="${VOLUMENAME-"${REPOSITORY_NAME,,}_data"}"
+ARCH="${ARCH-$(uname -m)}"
+PLATFORM="${PLATFORM-Linux/${ARCH}}"
+INVOKEAI_BRANCH="${INVOKEAI_BRANCH-$(git branch --show)}"
+CONTAINER_REGISTRY="${CONTAINER_REGISTRY-"ghcr.io"}"
+CONTAINER_REPOSITORY="${CONTAINER_REPOSITORY-"$(whoami)/${REPOSITORY_NAME}"}"
+CONTAINER_TAG="${CONTAINER_TAG-"${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}"}"
+CONTAINER_IMAGE="${CONTAINER_REGISTRY}/${CONTAINER_REPOSITORY}:${CONTAINER_TAG}"
+CONTAINER_IMAGE="${CONTAINER_IMAGE,,}"

docker/run.sh

@@ -1,8 +1,31 @@
 #!/usr/bin/env bash
 set -e
 
-SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
+# How to use: https://invoke-ai.github.io/InvokeAI/installation/INSTALL_DOCKER/#run-the-container
+# IMPORTANT: You need to have a token on huggingface.co to be able to download the checkpoints!!!
+
+SCRIPTDIR=$(dirname "$0")
 cd "$SCRIPTDIR" || exit 1
 
-docker-compose up --build -d
-docker-compose logs -f
+source ./env.sh
+
+echo -e "You are using these values:\n"
+echo -e "Volumename:\t${VOLUMENAME}"
+echo -e "Invokeai_tag:\t${CONTAINER_IMAGE}"
+echo -e "local Models:\t${MODELSPATH:-unset}\n"
+
+docker run \
+  --interactive \
+  --tty \
+  --rm \
+  --platform="${PLATFORM}" \
+  --name="${REPOSITORY_NAME,,}" \
+  --hostname="${REPOSITORY_NAME,,}" \
+  --mount=source="${VOLUMENAME}",target=/data \
+  ${MODELSPATH:+-u "$(id -u):$(id -g)"} \
+  ${MODELSPATH:+--mount="type=bind,source=${MODELSPATH},target=/data/models"} \
+  ${HUGGING_FACE_HUB_TOKEN:+--env="HUGGING_FACE_HUB_TOKEN=${HUGGING_FACE_HUB_TOKEN}"} \
+  --publish=9090:9090 \
+  --cap-add=sys_nice \
+  ${GPU_FLAGS:+--gpus="${GPU_FLAGS}"} \
+  "${CONTAINER_IMAGE}" ${1:+$@}

docker/runpod-readme.md

@@ -1,60 +0,0 @@
-# InvokeAI - A Stable Diffusion Toolkit
-
-Stable Diffusion distribution by InvokeAI: https://github.com/invoke-ai
-
-The Docker image tracks the `main` branch of the InvokeAI project, which means it includes the latest features, but may contain some bugs.
-
-Your working directory is mounted under the `/workspace` path inside the pod. The models are in `/workspace/invokeai/models`, and outputs are in `/workspace/invokeai/outputs`.
-
-> **Only the /workspace directory will persist between pod restarts!**
-
-> **If you _terminate_ (not just _stop_) the pod, the /workspace will be lost.**
-
-## Quickstart
-
-1. Launch a pod from this template. **It will take about 5-10 minutes to run through the initial setup**. Be patient.
-1. Wait for the application to load.
-    - TIP: you know it's ready when the CPU usage goes idle
-    - You can also check the logs for a line that says "_Point your browser at..._"
-1. Open the Invoke AI web UI: click the `Connect` => `connect over  HTTP` button.
-1. Generate some art!
-
-## Other things you can do
-
-At any point you may edit the pod configuration and set an arbitrary Docker command. For example, you could run a command to downloads some models using `curl`, or fetch some images and place them into your outputs to continue a working session.
-
-If you need to run *multiple commands*, define them in the Docker Command field like this:
-
-`bash -c "cd ${INVOKEAI_ROOT}/outputs; wormhole receive 2-foo-bar; invoke.py --web --host 0.0.0.0"`
-
-### Copying your data in and out of the pod
-
-This image includes a couple of handy tools to help you get the data into the pod (such as your custom models or embeddings), and out of the pod (such as downloading your outputs). Here are your options for getting your data in and out of the pod:
-
-- **SSH server**:
-    1. Make sure to create and set your Public Key in the RunPod settings (follow the official instructions)
-    1. Add an exposed port 22 (TCP) in the pod settings!
-    1. When your pod restarts, you will see a new entry in the `Connect` dialog. Use this SSH server to `scp` or `sftp` your files as necessary, or SSH into the pod using the fully fledged SSH server.
-
-- [**Magic Wormhole**](https://magic-wormhole.readthedocs.io/en/latest/welcome.html):
-    1. On your computer, `pip install magic-wormhole` (see above instructions for details)
-    1. Connect to the command line **using the "light" SSH client** or the browser-based console. _Currently there's a bug where `wormhole` isn't available when connected to "full" SSH server, as described above_.
-    1. `wormhole send /workspace/invokeai/outputs` will send the entire `outputs` directory. You can also send individual files.
-    1. Once packaged, you will see a `wormhole receive <123-some-words>` command. Copy it
-    1. Paste this command into the terminal on your local machine to securely download the payload.
-    1. It works the same in reverse: you can `wormhole send` some models from your computer to the pod. Again, save your files somewhere in `/workspace` or they will be lost when the pod is stopped.
-
-- **RunPod's Cloud Sync feature** may be used to sync the persistent volume to cloud storage. You could, for example, copy the entire `/workspace` to S3, add some custom models to it, and copy it back from S3 when launching new pod configurations. Follow the Cloud Sync instructions.
-
-
-### Disable the NSFW checker
-
-The NSFW checker is enabled by default. To disable it, edit the pod configuration and set the following command:
-
-```
-invoke --web --host 0.0.0.0 --no-nsfw_checker
-```
-
----
-
-Template ©2023 Eugene Brodsky [ebr](https://github.com/ebr)

docs/CHANGELOG.md

@@ -4,237 +4,7 @@ title: Changelog
 
 # :octicons-log-16: **Changelog**
 
-## v2.3.5 <small>(22 May 2023)</small>
-
-This release (along with the post1 and post2 follow-on releases) expands support for additional LoRA and LyCORIS models, upgrades diffusers versions, and fixes a few bugs.
-
-### LoRA and LyCORIS Support Improvement
-
-    A number of LoRA/LyCORIS fine-tune files (those which alter the text encoder as well as the unet model) were not having the desired effect in InvokeAI. This bug has now been fixed. Full documentation of LoRA support is available at InvokeAI LoRA Support.
-    Previously, InvokeAI did not distinguish between LoRA/LyCORIS models based on Stable Diffusion v1.5 vs those based on v2.0 and 2.1, leading to a crash when an incompatible model was loaded. This has now been fixed. In addition, the web pulldown menus for LoRA and Textual Inversion selection have been enhanced to show only those files that are compatible with the currently-selected Stable Diffusion model.
-    Support for the newer LoKR LyCORIS files has been added.
-
-### Library Updates and Speed/Reproducibility Advancements
-The major enhancement in this version is that NVIDIA users no longer need to decide between speed and reproducibility. Previously, if you activated the Xformers library, you would see improvements in speed and memory usage, but multiple images generated with the same seed and other parameters would be slightly different from each other. This is no longer the case. Relative to 2.3.5 you will see improved performance when running without Xformers, and even better performance when Xformers is activated. In both cases, images generated with the same settings will be identical.
-
-Here are the new library versions:
-Library 	Version
-Torch 	2.0.0
-Diffusers 	0.16.1
-Xformers 	0.0.19
-Compel 	1.1.5
-Other Improvements
-
-### Performance Improvements
-
-    When a model is loaded for the first time, InvokeAI calculates its checksum for incorporation into the PNG metadata. This process could take up to a minute on network-mounted disks and WSL mounts. This release noticeably speeds up the process.
-
-### Bug Fixes
-
-    The "import models from directory" and "import from URL" functionality in the console-based model installer has now been fixed.
-    When running the WebUI, we have reduced the number of times that InvokeAI reaches out to HuggingFace to fetch the list of embeddable Textual Inversion models. We have also caught and fixed a problem with the updater not correctly detecting when another instance of the updater is running 
-
-
-## v2.3.4 <small>(7 April 2023)</small>
-
-What's New in 2.3.4
-
-This features release adds support for LoRA (Low-Rank Adaptation) and LyCORIS (Lora beYond Conventional) models, as well as some minor bug fixes.
-### LoRA and LyCORIS Support
-
-LoRA files contain fine-tuning weights that enable particular styles, subjects or concepts to be applied to generated images. LyCORIS files are an extended variant of LoRA. InvokeAI supports the most common LoRA/LyCORIS format, which ends in the suffix .safetensors. You will find numerous LoRA and LyCORIS models for download at Civitai, and a small but growing number at Hugging Face. Full documentation of LoRA support is available at InvokeAI LoRA Support.( Pre-release note: this page will only be available after release)
-
-To use LoRA/LyCORIS models in InvokeAI:
-
-    Download the .safetensors files of your choice and place in /path/to/invokeai/loras. This directory was not present in earlier version of InvokeAI but will be created for you the first time you run the command-line or web client. You can also create the directory manually.
-
-    Add withLora(lora-file,weight) to your prompts. The weight is optional and will default to 1.0. A few examples, assuming that a LoRA file named loras/sushi.safetensors is present:
-
-family sitting at dinner table eating sushi withLora(sushi,0.9)
-family sitting at dinner table eating sushi withLora(sushi, 0.75)
-family sitting at dinner table eating sushi withLora(sushi)
-
-Multiple withLora() prompt fragments are allowed. The weight can be arbitrarily large, but the useful range is roughly 0.5 to 1.0. Higher weights make the LoRA's influence stronger. Negative weights are also allowed, which can lead to some interesting effects.
-
-    Generate as you usually would! If you find that the image is too "crisp" try reducing the overall CFG value or reducing individual LoRA weights. As is the case with all fine-tunes, you'll get the best results when running the LoRA on top of the model similar to, or identical with, the one that was used during the LoRA's training. Don't try to load a SD 1.x-trained LoRA into a SD 2.x model, and vice versa. This will trigger a non-fatal error message and generation will not proceed.
-
-    You can change the location of the loras directory by passing the --lora_directory option to `invokeai.
-
-### New WebUI LoRA and Textual Inversion Buttons
-
-This version adds two new web interface buttons for inserting LoRA and Textual Inversion triggers into the prompt as shown in the screenshot below.
-
-Clicking on one or the other of the buttons will bring up a menu of available LoRA/LyCORIS or Textual Inversion trigger terms. Select a menu item to insert the properly-formatted withLora() or <textual-inversion> prompt fragment into the positive prompt. The number in parentheses indicates the number of trigger terms currently in the prompt. You may click the button again and deselect the LoRA or trigger to remove it from the prompt, or simply edit the prompt directly.
-
-Currently terms are inserted into the positive prompt textbox only. However, some textual inversion embeddings are designed to be used with negative prompts. To move a textual inversion trigger into the negative prompt, simply cut and paste it.
-
-By default the Textual Inversion menu only shows locally installed models found at startup time in /path/to/invokeai/embeddings. However, InvokeAI has the ability to dynamically download and install additional Textual Inversion embeddings from the HuggingFace Concepts Library. You may choose to display the most popular of these (with five or more likes) in the Textual Inversion menu by going to Settings and turning on "Show Textual Inversions from HF Concepts Library." When this option is activated, the locally-installed TI embeddings will be shown first, followed by uninstalled terms from Hugging Face. See The Hugging Face Concepts Library and Importing Textual Inversion files for more information.
-### Minor features and fixes
-
-This release changes model switching behavior so that the command-line and Web UIs save the last model used and restore it the next time they are launched. It also improves the behavior of the installer so that the pip utility is kept up to date.
-  
-### Known Bugs in 2.3.4
-
-These are known bugs in the release.
-
-    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
-    Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
-
-
-## v2.3.3 <small>(28 March 2023)</small>
-
-This is a bugfix and minor feature release.
-### Bugfixes
-
-Since version 2.3.2 the following bugs have been fixed:
-Bugs
-
-    When using legacy checkpoints with an external VAE, the VAE file is now scanned for malware prior to loading. Previously only the main model weights file was scanned.
-    Textual inversion will select an appropriate batchsize based on whether xformers is active, and will default to xformers enabled if the library is detected.
-    The batch script log file names have been fixed to be compatible with Windows.
-    Occasional corruption of the .next_prefix file (which stores the next output file name in sequence) on Windows systems is now detected and corrected.
-    Support loading of legacy config files that have no personalization (textual inversion) section.
-    An infinite loop when opening the developer's console from within the invoke.sh script has been corrected.
-    Documentation fixes, including a recipe for detecting and fixing problems with the AMD GPU ROCm driver.
-
-Enhancements
-
-    It is now possible to load and run several community-contributed SD-2.0 based models, including the often-requested "Illuminati" model.
-    The "NegativePrompts" embedding file, and others like it, can now be loaded by placing it in the InvokeAI embeddings directory.
-    If no --model is specified at launch time, InvokeAI will remember the last model used and restore it the next time it is launched.
-    On Linux systems, the invoke.sh launcher now uses a prettier console-based interface. To take advantage of it, install the dialog package using your package manager (e.g. sudo apt install dialog).
-    When loading legacy models (safetensors/ckpt) you can specify a custom config file and/or a VAE by placing like-named files in the same directory as the model following this example:
-
-my-favorite-model.ckpt
-my-favorite-model.yaml
-my-favorite-model.vae.pt      # or my-favorite-model.vae.safetensors
-
-### Known Bugs in 2.3.3
-
-These are known bugs in the release.
-
-    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
-    Windows Defender will sometimes raise Trojan or backdoor alerts for the codeformer.pth face restoration model, as well as the CIDAS/clipseg and runwayml/stable-diffusion-v1.5 models. These are false positives and can be safely ignored. InvokeAI performs a malware scan on all models as they are loaded. For additional security, you should use safetensors models whenever they are available.
-
-
-## v2.3.2 <small>(11 March 2023)</small>
-This is a bugfix and minor feature release.
-
-### Bugfixes
-
-Since version 2.3.1 the following bugs have been fixed:
-
-    Black images appearing for potential NSFW images when generating with legacy checkpoint models and both --no-nsfw_checker and --ckpt_convert turned on.
-    Black images appearing when generating from models fine-tuned on Stable-Diffusion-2-1-base. When importing V2-derived models, you may be asked to select whether the model was derived from a "base" model (512 pixels) or the 768-pixel SD-2.1 model.
-    The "Use All" button was not restoring the Hi-Res Fix setting on the WebUI
-    When using the model installer console app, models failed to import correctly when importing from directories with spaces in their names. A similar issue with the output directory was also fixed.
-    Crashes that occurred during model merging.
-    Restore previous naming of Stable Diffusion base and 768 models.
-    Upgraded to latest versions of diffusers, transformers, safetensors and accelerate libraries upstream. We hope that this will fix the assertion NDArray > 2**32 issue that MacOS users have had when generating images larger than 768x768 pixels. Please report back.
-
-As part of the upgrade to diffusers, the location of the diffusers-based models has changed from models/diffusers to models/hub. When you launch InvokeAI for the first time, it will prompt you to OK a one-time move. This should be quick and harmless, but if you have modified your models/diffusers directory in some way, for example using symlinks, you may wish to cancel the migration and make appropriate adjustments.
-New "Invokeai-batch" script
-
-### Invoke AI Batch
-2.3.2 introduces a new command-line only script called invokeai-batch that can be used to generate hundreds of images from prompts and settings that vary systematically. This can be used to try the same prompt across multiple combinations of models, steps, CFG settings and so forth. It also allows you to template prompts and generate a combinatorial list like:
-
-a shack in the mountains, photograph
-a shack in the mountains, watercolor
-a shack in the mountains, oil painting
-a chalet in the mountains, photograph
-a chalet in the mountains, watercolor
-a chalet in the mountains, oil painting
-a shack in the desert, photograph
-...
-
-If you have a system with multiple GPUs, or a single GPU with lots of VRAM, you can parallelize generation across the combinatorial set, reducing wait times and using your system's resources efficiently (make sure you have good GPU cooling).
-
-To try invokeai-batch out. Launch the "developer's console" using the invoke launcher script, or activate the invokeai virtual environment manually. From the console, give the command invokeai-batch --help in order to learn how the script works and create your first template file for dynamic prompt generation.
-
-
-### Known Bugs in 2.3.2
-
-These are known bugs in the release.
-
-    The Ancestral DPMSolverMultistepScheduler (k_dpmpp_2a) sampler is not yet implemented for diffusers models and will disappear from the WebUI Sampler menu when a diffusers model is selected.
-    Windows Defender will sometimes raise a Trojan alert for the codeformer.pth face restoration model. As far as we have been able to determine, this is a false positive and can be safely whitelisted.
-
-
-## v2.3.1 <small>(22 February 2023)</small>
-This is primarily a bugfix release, but it does provide several new features that will improve the user experience.
-
-### Enhanced support for model management
-
-InvokeAI now makes it convenient to add, remove and modify models. You can individually import models that are stored on your local system, scan an entire folder and its subfolders for models and import them automatically, and even directly import models from the internet by providing their download URLs. You also have the option of designating a local folder to scan for new models each time InvokeAI is restarted.
-
-There are three ways of accessing the model management features:
-
-    From the WebUI, click on the cube to the right of the model selection menu. This will bring up a form that allows you to import models individually from your local disk or scan a directory for models to import.
-
-    Using the Model Installer App
-
-Choose option (5) download and install models from the invoke launcher script to start a new console-based application for model management. You can use this to select from a curated set of starter models, or import checkpoint, safetensors, and diffusers models from a local disk or the internet. The example below shows importing two checkpoint URLs from popular SD sites and a HuggingFace diffusers model using its Repository ID. It also shows how to designate a folder to be scanned at startup time for new models to import.
-
-Command-line users can start this app using the command invokeai-model-install.
-
-    Using the Command Line Client (CLI)
-
-The !install_model and !convert_model commands have been enhanced to allow entering of URLs and local directories to scan and import. The first command installs .ckpt and .safetensors files as-is. The second one converts them into the faster diffusers format before installation.
-
-Internally InvokeAI is able to probe the contents of a .ckpt or .safetensors file to distinguish among v1.x, v2.x and inpainting models. This means that you do not need to include "inpaint" in your model names to use an inpainting model. Note that Stable Diffusion v2.x models will be autoconverted into a diffusers model the first time you use it.
-
-Please see INSTALLING MODELS for more information on model management.
-
-### An Improved Installer Experience
-
-The installer now launches a console-based UI for setting and changing commonly-used startup options:
-
-After selecting the desired options, the installer installs several support models needed by InvokeAI's face reconstruction and upscaling features and then launches the interface for selecting and installing models shown earlier. At any time, you can edit the startup options by launching invoke.sh/invoke.bat and entering option (6) change InvokeAI startup options
-
-Command-line users can launch the new configure app using invokeai-configure.
-
-This release also comes with a renewed updater. To do an update without going through a whole reinstallation, launch invoke.sh or invoke.bat and choose option (9) update InvokeAI . This will bring you to a screen that prompts you to update to the latest released version, to the most current development version, or any released or unreleased version you choose by selecting the tag or branch of the desired version.
-
-Command-line users can run this interface by typing invokeai-configure
-
-### Image Symmetry Options
-
-There are now features to generate horizontal and vertical symmetry during generation. The way these work is to wait until a selected step in the generation process and then to turn on a mirror image effect. In addition to generating some cool images, you can also use this to make side-by-side comparisons of how an image will look with more or fewer steps. Access this option from the WebUI by selecting Symmetry from the image generation settings, or within the CLI by using the options --h_symmetry_time_pct and --v_symmetry_time_pct (these can be abbreviated to --h_sym and --v_sym like all other options).
-
-### A New Unified Canvas Look
-
-This release introduces a beta version of the WebUI Unified Canvas. To try it out, open up the settings dialogue in the WebUI (gear icon) and select Use Canvas Beta Layout:
-
-Refresh the screen and go to to Unified Canvas (left side of screen, third icon from the top). The new layout is designed to provide more space to work in and to keep the image controls close to the image itself:
-
-Model conversion and merging within the WebUI
-
-The WebUI now has an intuitive interface for model merging, as well as for permanent conversion of models from legacy .ckpt/.safetensors formats into diffusers format. These options are also available directly from the invoke.sh/invoke.bat scripts.
-An easier way to contribute translations to the WebUI
-
-We have migrated our translation efforts to Weblate, a FOSS translation product. Maintaining the growing project's translations is now far simpler for the maintainers and community. Please review our brief translation guide for more information on how to contribute.
-Numerous internal bugfixes and performance issues
-
-### Bug Fixes
-This releases quashes multiple bugs that were reported in 2.3.0. Major internal changes include upgrading to diffusers 0.13.0, and using the compel library for prompt parsing. See Detailed Change Log for a detailed list of bugs caught and squished.
-Summary of InvokeAI command line scripts (all accessible via the launcher menu)
-Command 	Description
-invokeai 	Command line interface
-invokeai --web 	Web interface
-invokeai-model-install 	Model installer with console forms-based front end
-invokeai-ti --gui 	Textual inversion, with a console forms-based front end
-invokeai-merge --gui 	Model merging, with a console forms-based front end
-invokeai-configure 	Startup configuration; can also be used to reinstall support models
-invokeai-update 	InvokeAI software updater
-
-### Known Bugs in 2.3.1
-
-These are known bugs in the release.
-    MacOS users generating 768x768 pixel images or greater using diffusers models may experience a hard crash with assertion NDArray > 2**32 This appears to be an issu...
-
-
-
-## v2.3.0 <small>(15 January 2023)</small>
+## v2.3.0 <small>(15 January 2023)</small>	
 
 **Transition to diffusers
 
@@ -274,7 +44,7 @@ introduces several changes you should know about.
   A configuration stanza for a diffuers model stored locally should
   look like this, with a `format` of `diffusers`, but a `path` field
   that points at the directory that contains `model_index.json`:
-
+  
   ```
   waifu-diffusion:
   description: Latest waifu diffusion 1.4
@@ -324,7 +94,7 @@ introduces several changes you should know about.
    !import_model /opt/sd-models/sd-1.4.ckpt
    !import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
    ```
-
+   
 **KNOWN BUGS (15 January 2023)
 
 1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
@@ -491,10 +261,10 @@ sections describe what's new for InvokeAI.
   [Installation](installation/index.md).
 - A streamlined manual installation process that works for both Conda and
   PIP-only installs. See
-  [Manual Installation](installation/020_INSTALL_MANUAL.md).
+  [Manual Installation](installation/INSTALL_MANUAL.md).
 - The ability to save frequently-used startup options (model to load, steps,
   sampler, etc) in a `.invokeai` file. See
-  [Client](deprecated/CLI.md)
+  [Client](features/CLI.md)
 - Support for AMD GPU cards (non-CUDA) on Linux machines.
 - Multiple bugs and edge cases squashed.
 
@@ -617,6 +387,8 @@ sections describe what's new for InvokeAI.
 - `dream.py` script renamed `invoke.py`. A `dream.py` script wrapper remains for
   backward compatibility.
 - Completely new WebGUI - launch with `python3 scripts/invoke.py --web`
+- Support for [inpainting](features/INPAINTING.md) and
+  [outpainting](features/OUTPAINTING.md)
 - img2img runs on all k\* samplers
 - Support for
   [negative prompts](features/PROMPTS.md#negative-and-unconditioned-prompts)
@@ -627,7 +399,7 @@ sections describe what's new for InvokeAI.
   using facial reconstruction, ESRGAN upscaling, outcropping (similar to DALL-E
   infinite canvas), and "embiggen" upscaling. See the `!fix` command.
 - New `--hires` option on `invoke>` line allows
-  [larger images to be created without duplicating elements](deprecated/CLI.md#this-is-an-example-of-txt2img),
+  [larger images to be created without duplicating elements](features/CLI.md#this-is-an-example-of-txt2img),
   at the cost of some performance.
 - New `--perlin` and `--threshold` options allow you to add and control
   variation during image generation (see
@@ -636,7 +408,7 @@ sections describe what's new for InvokeAI.
   of images and tweaking of previous settings.
 - Command-line completion in `invoke.py` now works on Windows, Linux and Mac
   platforms.
-- Improved [command-line completion behavior](deprecated/CLI.md) New commands
+- Improved [command-line completion behavior](features/CLI.md) New commands
   added:
   - List command-line history with `!history`
   - Search command-line history with `!search`

docs/contributing/ARCHITECTURE.md

@@ -1,93 +0,0 @@
-# Invoke.AI Architecture
-
-```mermaid
-flowchart TB
-
-  subgraph apps[Applications]
-    webui[WebUI]
-    cli[CLI]
-
-  subgraph webapi[Web API]
-    api[HTTP API]
-    sio[Socket.IO]
-  end
-
-  end
-
-  subgraph invoke[Invoke]
-    direction LR
-    invoker
-    services
-    sessions
-    invocations
-  end
-
-  subgraph core[AI Core]
-    Generate
-  end
-
-  webui --> webapi
-  webapi --> invoke
-  cli --> invoke
-
-  invoker --> services & sessions
-  invocations --> services
-  sessions --> invocations
-
-  services --> core
-
-  %% Styles
-  classDef sg fill:#5028C8,font-weight:bold,stroke-width:2,color:#fff,stroke:#14141A
-  classDef default stroke-width:2px,stroke:#F6B314,color:#fff,fill:#14141A
-
-  class apps,webapi,invoke,core sg
-
-```
-
-## Applications
-
-Applications are built on top of the invoke framework. They should construct `invoker` and then interact through it. They should avoid interacting directly with core code in order to support a variety of configurations.
-
-### Web UI
-
-The Web UI is built on top of an HTTP API built with [FastAPI](https://fastapi.tiangolo.com/) and [Socket.IO](https://socket.io/). The frontend code is found in `/frontend` and the backend code is found in `/ldm/invoke/app/api_app.py` and `/ldm/invoke/app/api/`. The code is further organized as such:
-
-| Component | Description |
-| --- | --- |
-| api_app.py | Sets up the API app, annotates the OpenAPI spec with additional data, and runs the API |
-| dependencies | Creates all invoker services and the invoker, and provides them to the API |
-| events | An eventing system that could in the future be adapted to support horizontal scale-out |
-| sockets | The Socket.IO interface - handles listening to and emitting session events (events are defined in the events service module) |
-| routers | API definitions for different areas of API functionality |
-
-### CLI
-
-The CLI is built automatically from invocation metadata, and also supports invocation piping and auto-linking. Code is available in `/ldm/invoke/app/cli_app.py`.
-
-## Invoke
-
-The Invoke framework provides the interface to the underlying AI systems and is built with flexibility and extensibility in mind. There are four major concepts: invoker, sessions, invocations, and services.
-
-### Invoker
-
-The invoker (`/ldm/invoke/app/services/invoker.py`) is the primary interface through which applications interact with the framework. Its primary purpose is to create, manage, and invoke sessions. It also maintains two sets of services:
-- **invocation services**, which are used by invocations to interact with core functionality.
-- **invoker services**, which are used by the invoker to manage sessions and manage the invocation queue.
-
-### Sessions
-
-Invocations and links between them form a graph, which is maintained in a session. Sessions can be queued for invocation, which will execute their graph (either the next ready invocation, or all invocations). Sessions also maintain execution history for the graph (including storage of any outputs). An invocation may be added to a session at any time, and there is capability to add and entire graph at once, as well as to automatically link new invocations to previous invocations. Invocations can not be deleted or modified once added.
-
-The session graph does not support looping. This is left as an application problem to prevent additional complexity in the graph.
-
-### Invocations
-
-Invocations represent individual units of execution, with inputs and outputs. All invocations are located in `/ldm/invoke/app/invocations`, and are all automatically discovered and made available in the applications. These are the primary way to expose new functionality in Invoke.AI, and the [implementation guide](INVOCATIONS.md) explains how to add new invocations.
-
-### Services
-
-Services provide invocations access AI Core functionality and other necessary functionality (e.g. image storage). These are available in `/ldm/invoke/app/services`. As a general rule, new services should provide an interface as an abstract base class, and may provide a lightweight local implementation by default in their module. The goal for all services should be to enable the usage of different implementations (e.g. using cloud storage for image storage), but should not load any module dependencies unless that implementation has been used (i.e. don't import anything that won't be used, especially if it's expensive to import).
-
-## AI Core
-
-The AI Core is represented by the rest of the code base (i.e. the code outside of `/ldm/invoke/app/`).

docs/contributing/CONTRIBUTING.md

@@ -1,54 +0,0 @@
-## Welcome to Invoke AI
-
-We're thrilled to have you here and we're excited for you to contribute. 
-
-Invoke AI originated as a project built by the community, and that vision carries forward today as we aim to build the best pro-grade tools available. We work together to incorporate the latest in AI/ML research, making these tools available in over 20 languages to artists and creatives around the world as part of our fully permissive OSS project designed for individual users to self-host and use.
-
-Here are some guidelines to help you get started:
-
-### Technical Prerequisites
-
-Front-end: You'll need a working knowledge of React and TypeScript.
-
-Back-end: Depending on the scope of your contribution, you may need to know SQLite, FastAPI, Python, and Socketio. Also, a good majority of the backend logic involved in processing images is built in a modular way using a concept called "Nodes", which are isolated functions that carry out individual, discrete operations. This design allows for easy contributions of novel pipelines and capabilities.
-
-### How to Submit Contributions
-
-To start contributing, please follow these steps:
-
-1. Familiarize yourself with our roadmap and open projects to see where your skills and interests align. These documents can serve as a source of inspiration.
-2. Open a Pull Request (PR) with a clear description of the feature you're adding or the problem you're solving. Make sure your contribution aligns with the project's vision.
-3. Adhere to general best practices. This includes assuming interoperability with other nodes, keeping the scope of your functions as small as possible, and organizing your code according to our architecture documents.
-
-### Types of Contributions We're Looking For
-
-We welcome all contributions that improve the project. Right now, we're especially looking for:
-
-1. Quality of life (QOL) enhancements on the front-end.
-2. New backend capabilities added through nodes.
-3. Incorporating additional optimizations from the broader open-source software community.
-
-### Communication and Decision-making Process
-
-Project maintainers and code owners review PRs to ensure they align with the project's goals. They may provide design or architectural guidance, suggestions on user experience, or provide more significant feedback on the contribution itself. Expect to receive feedback on your submissions, and don't hesitate to ask questions or propose changes.
-
-For more robust discussions, or if you're planning to add capabilities not currently listed on our roadmap, please reach out to us on our Discord server. That way, we can ensure your proposed contribution aligns with the project's direction before you start writing code.
-
-### Code of Conduct and Contribution Expectations
-
-We want everyone in our community to have a positive experience. To facilitate this, we've established a code of conduct and a statement of values that we expect all contributors to adhere to. Please take a moment to review these documents—they're essential to maintaining a respectful and inclusive environment.
-
-By making a contribution to this project, you certify that:
-
-1. The contribution was created in whole or in part by you and you have the right to submit it under the open-source license indicated in this project’s GitHub repository; or
-2. The contribution is based upon previous work that, to the best of your knowledge, is covered under an appropriate open-source license and you have the right under that license to submit that work with modifications, whether created in whole or in part by you, under the same open-source license (unless you are permitted to submit under a different license); or
-3. The contribution was provided directly to you by some other person who certified (1) or (2) and you have not modified it; or
-4. You understand and agree that this project and the contribution are public and that a record of the contribution (including all personal information you submit with it, including your sign-off) is maintained indefinitely and may be redistributed consistent with this project or the open-source license(s) involved.
-
-This disclaimer is not a license and does not grant any rights or permissions. You must obtain necessary permissions and licenses, including from third parties, before contributing to this project.
-
-This disclaimer is provided "as is" without warranty of any kind, whether expressed or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the contribution or the use or other dealings in the contribution.
-
----
-
-Remember, your contributions help make this project great. We're excited to see what you'll bring to our community!

docs/contributing/INVOCATIONS.md

@@ -1,790 +0,0 @@
-# Invocations
-
-Features in InvokeAI are added in the form of modular node-like systems called
-**Invocations**.
-
-An Invocation is simply a single operation that takes in some inputs and gives
-out some outputs. We can then chain multiple Invocations together to create more
-complex functionality.
-
-## Invocations Directory
-
-InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
-
-You can add your new functionality to one of the existing Invocations in this
-directory or create a new file in this directory as per your needs.
-
-**Note:** _All Invocations must be inside this directory for InvokeAI to
-recognize them as valid Invocations._
-
-## Creating A New Invocation
-
-In order to understand the process of creating a new Invocation, let us actually
-create one.
-
-In our example, let us create an Invocation that will take in an image, resize
-it and output the resized image.
-
-The first set of things we need to do when creating a new Invocation are -
-
-- Create a new class that derives from a predefined parent class called
-  `BaseInvocation`.
-- The name of every Invocation must end with the word `Invocation` in order for
-  it to be recognized as an Invocation.
-- Every Invocation must have a `docstring` that describes what this Invocation
-  does.
-- Every Invocation must have a unique `type` field defined which becomes its
-  indentifier.
-- Invocations are strictly typed. We make use of the native
-  [typing](https://docs.python.org/3/library/typing.html) library and the
-  installed [pydantic](https://pydantic-docs.helpmanual.io/) library for
-  validation.
-
-So let us do that.
-
-```python
-from typing import Literal
-from .baseinvocation import BaseInvocation
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-```
-
-That's great.
-
-Now we have setup the base of our new Invocation. Let us think about what inputs
-our Invocation takes.
-
-- We need an `image` that we are going to resize.
-- We will need new `width` and `height` values to which we need to resize the
-  image to.
-
-### **Inputs**
-
-Every Invocation input is a pydantic `Field` and like everything else should be
-strictly typed and defined.
-
-So let us create these inputs for our Invocation. First up, the `image` input we
-need. Generally, we can use standard variable types in Python but InvokeAI
-already has a custom `ImageField` type that handles all the stuff that is needed
-for image inputs.
-
-But what is this `ImageField` ..? It is a special class type specifically
-written to handle how images are dealt with in InvokeAI. We will cover how to
-create your own custom field types later in this guide. For now, let's go ahead
-and use it.
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation
-from ..models.image import ImageField
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-```
-
-Let us break down our input code.
-
-```python
-image: Union[ImageField, None] = Field(description="The input image", default=None)
-```
-
-| Part      | Value                                                | Description                                                                                        |
-| --------- | ---------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
-| Name      | `image`                                              | The variable that will hold our image                                                              |
-| Type Hint | `Union[ImageField, None]`                            | The types for our field. Indicates that the image can either be an `ImageField` type or `None`     |
-| Field     | `Field(description="The input image", default=None)` | The image variable is a field which needs a description and a default value that we set to `None`. |
-
-Great. Now let us create our other inputs for `width` and `height`
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation
-from ..models.image import ImageField
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
-```
-
-As you might have noticed, we added two new parameters to the field type for
-`width` and `height` called `gt` and `le`. These basically stand for _greater
-than or equal to_ and _less than or equal to_. There are various other param
-types for field that you can find on the **pydantic** documentation.
-
-**Note:** _Any time it is possible to define constraints for our field, we
-should do it so the frontend has more information on how to parse this field._
-
-Perfect. We now have our inputs. Let us do something with these.
-
-### **Invoke Function**
-
-The `invoke` function is where all the magic happens. This function provides you
-the `context` parameter that is of the type `InvocationContext` which will give
-you access to the current context of the generation and all the other services
-that are provided by it by InvokeAI.
-
-Let us create this function first.
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation, InvocationContext
-from ..models.image import ImageField
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
-
-    def invoke(self, context: InvocationContext):
-        pass
-```
-
-### **Outputs**
-
-The output of our Invocation will be whatever is returned by this `invoke`
-function. Like with our inputs, we need to strongly type and define our outputs
-too.
-
-What is our output going to be? Another image. Normally you'd have to create a
-type for this but InvokeAI already offers you an `ImageOutput` type that handles
-all the necessary info related to image outputs. So let us use that.
-
-We will cover how to create your own output types later in this guide.
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation, InvocationContext
-from ..models.image import ImageField
-from .image import ImageOutput
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        pass
-```
-
-Perfect. Now that we have our Invocation setup, let us do what we want to do.
-
-- We will first load the image. Generally we do this using the `PIL` library but
-  we can use one of the services provided by InvokeAI to load the image.
-- We will resize the image using `PIL` to our input data.
-- We will output this image in the format we set above.
-
-So let's do that.
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation, InvocationContext
-from ..models.image import ImageField, ResourceOrigin, ImageCategory
-from .image import ImageOutput
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the image using InvokeAI's predefined Image Service.
-        image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
-
-        # Resizing the image
-        # Because we used the above service, we already have a PIL image. So we can simply resize.
-        resized_image = image.resize((self.width, self.height))
-
-        # Preparing the image for output using InvokeAI's predefined Image Service.
-        output_image = context.services.images.create(
-            image=resized_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        # Returning the Image
-        return ImageOutput(
-            image=ImageField(
-                image_name=output_image.image_name,
-                image_origin=output_image.image_origin,
-            ),
-            width=output_image.width,
-            height=output_image.height,
-        )
-```
-
-**Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
-certain way that the images need to be dispatched in order to be stored and read
-correctly. In 99% of the cases when dealing with an image output, you can simply
-copy-paste the template above.
-
-That's it. You made your own **Resize Invocation**.
-
-## Result
-
-Once you make your Invocation correctly, the rest of the process is fully
-automated for you.
-
-When you launch InvokeAI, you can go to `http://localhost:9090/docs` and see
-your new Invocation show up there with all the relevant info.
-
-![resize invocation](../assets/contributing/resize_invocation.png)
-
-When you launch the frontend UI, you can go to the Node Editor tab and find your
-new Invocation ready to be used.
-
-![resize node editor](../assets/contributing/resize_node_editor.png)
-
-# Advanced
-
-## Custom Input Fields
-
-Now that you know how to create your own Invocations, let us dive into slightly
-more advanced topics.
-
-While creating your own Invocations, you might run into a scenario where the
-existing input types in InvokeAI do not meet your requirements. In such cases,
-you can create your own input types.
-
-Let us create one as an example. Let us say we want to create a color input
-field that represents a color code. But before we start on that here are some
-general good practices to keep in mind.
-
-**Good Practices**
-
-- There is no naming convention for input fields but we highly recommend that
-  you name it something appropriate like `ColorField`.
-- It is not mandatory but it is heavily recommended to add a relevant
-  `docstring` to describe your input field.
-- Keep your field in the same file as the Invocation that it is made for or in
-  another file where it is relevant.
-
-All input types a class that derive from the `BaseModel` type from `pydantic`.
-So let's create one.
-
-```python
-from pydantic import BaseModel
-
-class ColorField(BaseModel):
-    '''A field that holds the rgba values of a color'''
-    pass
-```
-
-Perfect. Now let us create our custom inputs for our field. This is exactly
-similar how you created input fields for your Invocation. All the same rules
-apply. Let us create four fields representing the _red(r)_, _blue(b)_,
-_green(g)_ and _alpha(a)_ channel of the color.
-
-```python
-class ColorField(BaseModel):
-    '''A field that holds the rgba values of a color'''
-    r: int = Field(ge=0, le=255, description="The red channel")
-    g: int = Field(ge=0, le=255, description="The green channel")
-    b: int = Field(ge=0, le=255, description="The blue channel")
-    a: int = Field(ge=0, le=255, description="The alpha channel")
-```
-
-That's it. We now have a new input field type that we can use in our Invocations
-like this.
-
-```python
-color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
-```
-
-**Extra Config**
-
-All input fields also take an additional `Config` class that you can use to do
-various advanced things like setting required parameters and etc.
-
-Let us do that for our _ColorField_ and enforce all the values because we did
-not define any defaults for our fields.
-
-```python
-class ColorField(BaseModel):
-    '''A field that holds the rgba values of a color'''
-    r: int = Field(ge=0, le=255, description="The red channel")
-    g: int = Field(ge=0, le=255, description="The green channel")
-    b: int = Field(ge=0, le=255, description="The blue channel")
-    a: int = Field(ge=0, le=255, description="The alpha channel")
-
-    class Config:
-        schema_extra = {"required": ["r", "g", "b", "a"]}
-```
-
-Now it becomes mandatory for the user to supply all the values required by our
-input field.
-
-We will discuss the `Config` class in extra detail later in this guide and how
-you can use it to make your Invocations more robust.
-
-## Custom Output Types
-
-Like with custom inputs, sometimes you might find yourself needing custom
-outputs that InvokeAI does not provide. We can easily set one up.
-
-Now that you are familiar with Invocations and Inputs, let us use that knowledge
-to put together a custom output type for an Invocation that returns _width_,
-_height_ and _background_color_ that we need to create a blank image.
-
-- A custom output type is a class that derives from the parent class of
-  `BaseInvocationOutput`.
-- It is not mandatory but we recommend using names ending with `Output` for
-  output types. So we'll call our class `BlankImageOutput`
-- It is not mandatory but we highly recommend adding a `docstring` to describe
-  what your output type is for.
-- Like Invocations, each output type should have a `type` variable that is
-  **unique**
-
-Now that we know the basic rules for creating a new output type, let us go ahead
-and make it.
-
-```python
-from typing import Literal
-from pydantic import Field
-
-from .baseinvocation import BaseInvocationOutput
-
-class BlankImageOutput(BaseInvocationOutput):
-    '''Base output type for creating a blank image'''
-    type: Literal['blank_image_output'] = 'blank_image_output'
-
-    # Inputs
-    width: int = Field(description='Width of blank image')
-    height: int = Field(description='Height of blank image')
-    bg_color: ColorField = Field(description='Background color of blank image')
-
-    class Config:
-        schema_extra = {"required": ["type", "width", "height", "bg_color"]}
-```
-
-All set. We now have an output type that requires what we need to create a
-blank_image. And if you noticed it, we even used the `Config` class to ensure
-the fields are required.
-
-## Custom Configuration
-
-As you might have noticed when making inputs and outputs, we used a class called
-`Config` from _pydantic_ to further customize them. Because our inputs and
-outputs essentially inherit from _pydantic_'s `BaseModel` class, all
-[configuration options](https://docs.pydantic.dev/latest/usage/schema/#schema-customization)
-that are valid for _pydantic_ classes are also valid for our inputs and outputs.
-You can do the same for your Invocations too but InvokeAI makes our life a
-little bit easier on that end.
-
-InvokeAI provides a custom configuration class called `InvocationConfig`
-particularly for configuring Invocations. This is exactly the same as the raw
-`Config` class from _pydantic_ with some extra stuff on top to help faciliate
-parsing of the scheme in the frontend UI.
-
-At the current moment, tihs `InvocationConfig` class is further improved with
-the following features related the `ui`.
-
-| Config Option | Field Type                                                                                                    | Example                                                                                                               |
-| ------------- | ------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
-| type_hints    | `Dict[str, Literal["integer", "float", "boolean", "string", "enum", "image", "latents", "model", "control"]]` | `type_hint: "model"` provides type hints related to the model like displaying a list of available models              |
-| tags          | `List[str]`                                                                                                   | `tags: ['resize', 'image']` will classify your invocation under the tags of resize and image.                         |
-| title         | `str`                                                                                                         | `title: 'Resize Image` will rename your to this custom title rather than infer from the name of the Invocation class. |
-
-So let us update your `ResizeInvocation` with some extra configuration and see
-how that works.
-
-```python
-from typing import Literal, Union
-from pydantic import Field
-
-from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
-from ..models.image import ImageField, ResourceOrigin, ImageCategory
-from .image import ImageOutput
-
-class ResizeInvocation(BaseInvocation):
-    '''Resizes an image'''
-    type: Literal['resize'] = 'resize'
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
-    height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
-
-    class Config(InvocationConfig):
-        schema_extra: {
-            ui: {
-                tags: ['resize', 'image'],
-                title: ['My Custom Resize']
-            }
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the image using InvokeAI's predefined Image Service.
-        image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
-
-        # Resizing the image
-        # Because we used the above service, we already have a PIL image. So we can simply resize.
-        resized_image = image.resize((self.width, self.height))
-
-        # Preparing the image for output using InvokeAI's predefined Image Service.
-        output_image = context.services.images.create(
-            image=resized_image,
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        # Returning the Image
-        return ImageOutput(
-            image=ImageField(
-                image_name=output_image.image_name,
-                image_origin=output_image.image_origin,
-            ),
-            width=output_image.width,
-            height=output_image.height,
-        )
-```
-
-We now customized our code to let the frontend know that our Invocation falls
-under `resize` and `image` categories. So when the user searches for these
-particular words, our Invocation will show up too.
-
-We also set a custom title for our Invocation. So instead of being called
-`Resize`, it will be called `My Custom Resize`.
-
-As simple as that.
-
-As time goes by, InvokeAI will further improve and add more customizability for
-Invocation configuration. We will have more documentation regarding this at a
-later time.
-
-# **[TODO]**
-
-## Custom Components For Frontend
-
-Every backend input type should have a corresponding frontend component so the
-UI knows what to render when you use a particular field type.
-
-If you are using existing field types, we already have components for those. So
-you don't have to worry about creating anything new. But this might not always
-be the case. Sometimes you might want to create new field types and have the
-frontend UI deal with it in a different way.
-
-This is where we venture into the world of React and Javascript and create our
-own new components for our Invocations. Do not fear the world of JS. It's
-actually pretty straightforward.
-
-Let us create a new component for our custom color field we created above. When
-we use a color field, let us say we want the UI to display a color picker for
-the user to pick from rather than entering values. That is what we will build
-now.
-
----
-
-# OLD -- TO BE DELETED OR MOVED LATER
-
----
-
-## Creating a new invocation
-
-To create a new invocation, either find the appropriate module file in
-`/ldm/invoke/app/invocations` to add your invocation to, or create a new one in
-that folder. All invocations in that folder will be discovered and made
-available to the CLI and API automatically. Invocations make use of
-[typing](https://docs.python.org/3/library/typing.html) and
-[pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration
-into the CLI and API.
-
-An invocation looks like this:
-
-```py
-class UpscaleInvocation(BaseInvocation):
-    """Upscales an image."""
-
-    # fmt: off
-    type: Literal["upscale"] = "upscale"
-
-    # Inputs
-    image: Union[ImageField, None] = Field(description="The input image", default=None)
-    strength: float                = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2, 4]           = Field(default=2, description="The upscale level")
-    # fmt: on
-
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
-
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        results = context.services.restoration.upscale_and_reconstruct(
-            image_list=[[image, 0]],
-            upscale=(self.level, self.strength),
-            strength=0.0,  # GFPGAN strength
-            save_original=False,
-            image_callback=None,
-        )
-
-        # Results are image and seed, unwrap for now
-        # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
-```
-
-Each portion is important to implement correctly.
-
-### Class definition and type
-
-```py
-class UpscaleInvocation(BaseInvocation):
-    """Upscales an image."""
-    type: Literal['upscale'] = 'upscale'
-```
-
-All invocations must derive from `BaseInvocation`. They should have a docstring
-that declares what they do in a single, short line. They should also have a
-`type` with a type hint that's `Literal["command_name"]`, where `command_name`
-is what the user will type on the CLI or use in the API to create this
-invocation. The `command_name` must be unique. The `type` must be assigned to
-the value of the literal in the type hint.
-
-### Inputs
-
-```py
-    # Inputs
-    image: Union[ImageField,None] = Field(description="The input image")
-    strength: float               = Field(default=0.75, gt=0, le=1, description="The strength")
-    level: Literal[2,4]           = Field(default=2, description="The upscale level")
-```
-
-Inputs consist of three parts: a name, a type hint, and a `Field` with default,
-description, and validation information. For example:
-
-| Part      | Value                                                         | Description                                                                                                               |
-| --------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
-| Name      | `strength`                                                    | This field is referred to as `strength`                                                                                   |
-| Type Hint | `float`                                                       | This field must be of type `float`                                                                                        |
-| Field     | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
-
-Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this
-field to be parsed with `None` as a value, which enables linking to previous
-invocations. All fields should either provide a default value or allow `None` as
-a value, so that they can be overwritten with a linked output from another
-invocation.
-
-The special type `ImageField` is also used here. All images are passed as
-`ImageField`, which protects them from pydantic validation errors (since images
-only ever come from links).
-
-Finally, note that for all linking, the `type` of the linked fields must match.
-If the `name` also matches, then the field can be **automatically linked** to a
-previous invocation by name and matching.
-
-### Config
-
-```py
-    # Schema customisation
-    class Config(InvocationConfig):
-        schema_extra = {
-            "ui": {
-                "tags": ["upscaling", "image"],
-            },
-        }
-```
-
-This is an optional configuration for the invocation. It inherits from
-pydantic's model `Config` class, and it used primarily to customize the
-autogenerated OpenAPI schema.
-
-The UI relies on the OpenAPI schema in two ways:
-
-- An API client & Typescript types are generated from it. This happens at build
-  time.
-- The node editor parses the schema into a template used by the UI to create the
-  node editor UI. This parsing happens at runtime.
-
-In this example, a `ui` key has been added to the `schema_extra` dict to provide
-some tags for the UI, to facilitate filtering nodes.
-
-See the Schema Generation section below for more information.
-
-### Invoke Function
-
-```py
-    def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.services.images.get_pil_image(
-            self.image.image_origin, self.image.image_name
-        )
-        results = context.services.restoration.upscale_and_reconstruct(
-            image_list=[[image, 0]],
-            upscale=(self.level, self.strength),
-            strength=0.0,  # GFPGAN strength
-            save_original=False,
-            image_callback=None,
-        )
-
-        # Results are image and seed, unwrap for now
-        # TODO: can this return multiple results?
-        image_dto = context.services.images.create(
-            image=results[0][0],
-            image_origin=ResourceOrigin.INTERNAL,
-            image_category=ImageCategory.GENERAL,
-            node_id=self.id,
-            session_id=context.graph_execution_state_id,
-            is_intermediate=self.is_intermediate,
-        )
-
-        return ImageOutput(
-            image=ImageField(
-                image_name=image_dto.image_name,
-                image_origin=image_dto.image_origin,
-            ),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-```
-
-The `invoke` function is the last portion of an invocation. It is provided an
-`InvocationContext` which contains services to perform work as well as a
-`session_id` for use as needed. It should return a class with output values that
-derives from `BaseInvocationOutput`.
-
-Before being called, the invocation will have all of its fields set from
-defaults, inputs, and finally links (overriding in that order).
-
-Assume that this invocation may be running simultaneously with other
-invocations, may be running on another machine, or in other interesting
-scenarios. If you need functionality, please provide it as a service in the
-`InvocationServices` class, and make sure it can be overridden.
-
-### Outputs
-
-```py
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
-```
-
-Output classes look like an invocation class without the invoke method. Prefer
-to use an existing output class if available, and prefer to name inputs the same
-as outputs when possible, to promote automatic invocation linking.
-
-## Schema Generation
-
-Invocation, output and related classes are used to generate an OpenAPI schema.
-
-### Required Properties
-
-The schema generation treat all properties with default values as optional. This
-makes sense internally, but when when using these classes via the generated
-schema, we end up with e.g. the `ImageOutput` class having its `image` property
-marked as optional.
-
-We know that this property will always be present, so the additional logic
-needed to always check if the property exists adds a lot of extraneous cruft.
-
-To fix this, we can leverage `pydantic`'s
-[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
-to mark properties that we know will always be present as required.
-
-Here's that `ImageOutput` class, without the needed schema customisation:
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-```
-
-The OpenAPI schema that results from this `ImageOutput` will have the `type`,
-`image`, `width` and `height` properties marked as optional, even though we know
-they will always have a value.
-
-```python
-class ImageOutput(BaseInvocationOutput):
-    """Base class for invocations that output an image"""
-
-    # fmt: off
-    type: Literal["image_output"] = "image_output"
-    image:      ImageField = Field(default=None, description="The output image")
-    width:             int = Field(description="The width of the image in pixels")
-    height:            int = Field(description="The height of the image in pixels")
-    # fmt: on
-
-    # Add schema customization
-    class Config:
-        schema_extra = {"required": ["type", "image", "width", "height"]}
-```
-
-With the customization in place, the schema will now show these properties as
-required, obviating the need for extensive null checks in client code.
-
-See this `pydantic` issue for discussion on this solution:
-<https://github.com/pydantic/pydantic/discussions/4577>

docs/contributing/LOCAL_DEVELOPMENT.md

@@ -1,273 +0,0 @@
-# Local Development
-
-If you are looking to contribute you will need to have a local development
-environment. See the
-[Developer Install](../installation/020_INSTALL_MANUAL.md#developer-install) for
-full details.
-
-Broadly this involves cloning the repository, installing the pre-reqs, and
-InvokeAI (in editable form). Assuming this is working, choose your area of
-focus.
-
-## Documentation
-
-We use [mkdocs](https://www.mkdocs.org) for our documentation with the
-[material theme](https://squidfunk.github.io/mkdocs-material/). Documentation is
-written in markdown files under the `./docs` folder and then built into a static
-website for hosting with GitHub Pages at
-[invoke-ai.github.io/InvokeAI](https://invoke-ai.github.io/InvokeAI).
-
-To contribute to the documentation you'll need to install the dependencies. Note
-the use of `"`.
-
-```zsh
-pip install ".[docs]"
-```
-
-Now, to run the documentation locally with hot-reloading for changes made.
-
-```zsh
-mkdocs serve
-```
-
-You'll then be prompted to connect to `http://127.0.0.1:8080` in order to
-access.
-
-## Backend
-
-The backend is contained within the `./invokeai/backend` folder structure. To
-get started however please install the development dependencies.
-
-From the root of the repository run the following command. Note the use of `"`.
-
-```zsh
-pip install ".[test]"
-```
-
-This in an optional group of packages which is defined within the
-`pyproject.toml` and will be required for testing the changes you make the the
-code.
-
-### Running Tests
-
-We use [pytest](https://docs.pytest.org/en/7.2.x/) for our test suite. Tests can
-be found under the `./tests` folder and can be run with a single `pytest`
-command. Optionally, to review test coverage you can append `--cov`.
-
-```zsh
-pytest --cov
-```
-
-Test outcomes and coverage will be reported in the terminal. In addition a more
-detailed report is created in both XML and HTML format in the `./coverage`
-folder. The HTML one in particular can help identify missing statements
-requiring tests to ensure coverage. This can be run by opening
-`./coverage/html/index.html`.
-
-For example.
-
-```zsh
-pytest --cov; open ./coverage/html/index.html
-```
-
-??? info "HTML coverage report output"
-
-    ![html-overview](../assets/contributing/html-overview.png)
-
-    ![html-detail](../assets/contributing/html-detail.png)
-
-## Front End
-
-<!--#TODO: get input from blessedcoolant here, for the moment inserted the frontend README via snippets extension.-->
-
---8<-- "invokeai/frontend/web/README.md"
-
-## Developing InvokeAI in VSCode
-
-VSCode offers some nice tools:
-
-- python debugger
-- automatic `venv` activation
-- remote dev (e.g. run InvokeAI on a beefy linux desktop while you type in
-  comfort on your macbook)
-
-### Setup
-
-You'll need the
-[Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
-and
-[Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
-extensions installed first.
-
-It's also really handy to install the `Jupyter` extensions:
-
-- [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
-- [Jupyter Cell Tags](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-cell-tags)
-- [Jupyter Notebook Renderers](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter-renderers)
-- [Jupyter Slide Show](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.vscode-jupyter-slideshow)
-
-#### InvokeAI workspace
-
-Creating a VSCode workspace for working on InvokeAI is highly recommended. It
-can hold InvokeAI-specific settings and configs.
-
-To make a workspace:
-
-- Open the InvokeAI repo dir in VSCode
-- `File` > `Save Workspace As` > save it _outside_ the repo
-
-#### Default python interpreter (i.e. automatic virtual environment activation)
-
-- Use command palette to run command
-  `Preferences: Open Workspace Settings (JSON)`
-- Add `python.defaultInterpreterPath` to `settings`, pointing to your `venv`'s
-  python
-
-Should look something like this:
-
-```jsonc
-{
-  // I like to have all InvokeAI-related folders in my workspace
-  "folders": [
-    {
-      // repo root
-      "path": "InvokeAI"
-    },
-    {
-      // InvokeAI root dir, where `invokeai.yaml` lives
-      "path": "/path/to/invokeai_root"
-    }
-  ],
-  "settings": {
-    // Where your InvokeAI `venv`'s python executable lives
-    "python.defaultInterpreterPath": "/path/to/invokeai_root/.venv/bin/python"
-  }
-}
-```
-
-Now when you open the VSCode integrated terminal, or do anything that needs to
-run python, it will automatically be in your InvokeAI virtual environment.
-
-Bonus: When you create a Jupyter notebook, when you run it, you'll be prompted
-for the python interpreter to run in. This will default to your `venv` python,
-and so you'll have access to the same python environment as the InvokeAI app.
-
-This is _super_ handy.
-
-#### Debugging configs with `launch.json`
-
-Debugging configs are managed in a `launch.json` file. Like most VSCode configs,
-these can be scoped to a workspace or folder.
-
-Follow the [official guide](https://code.visualstudio.com/docs/python/debugging)
-to set up your `launch.json` and try it out.
-
-Now we can create the InvokeAI debugging configs:
-
-```jsonc
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      // Run the InvokeAI backend & serve the pre-built UI
-      "name": "InvokeAI Web",
-      "type": "python",
-      "request": "launch",
-      "program": "scripts/invokeai-web.py",
-      "args": [
-        // Your InvokeAI root dir (where `invokeai.yaml` lives)
-        "--root",
-        "/path/to/invokeai_root",
-        // Access the app from anywhere on your local network
-        "--host",
-        "0.0.0.0"
-      ],
-      "justMyCode": true
-    },
-    {
-      // Run the nodes-based CLI
-      "name": "InvokeAI CLI",
-      "type": "python",
-      "request": "launch",
-      "program": "scripts/invokeai-cli.py",
-      "justMyCode": true
-    },
-    {
-      // Run tests
-      "name": "InvokeAI Test",
-      "type": "python",
-      "request": "launch",
-      "module": "pytest",
-      "args": ["--capture=no"],
-      "justMyCode": true
-    },
-    {
-      // Run a single test
-      "name": "InvokeAI Single Test",
-      "type": "python",
-      "request": "launch",
-      "module": "pytest",
-      "args": [
-        // Change this to point to the specific test you are working on
-        "tests/nodes/test_invoker.py"
-      ],
-      "justMyCode": true
-    },
-    {
-      // This is the default, useful to just run a single file
-      "name": "Python: File",
-      "type": "python",
-      "request": "launch",
-      "program": "${file}",
-      "justMyCode": true
-    }
-  ]
-}
-```
-
-You'll see these configs in the debugging configs drop down. Running them will
-start InvokeAI with attached debugger, in the correct environment, and work just
-like the normal app.
-
-Enjoy debugging InvokeAI with ease (not that we have any bugs of course).
-
-#### Remote dev
-
-This is very easy to set up and provides the same very smooth experience as
-local development. Environments and debugging, as set up above, just work,
-though you'd need to recreate the workspace and debugging configs on the remote.
-
-Consult the
-[official guide](https://code.visualstudio.com/docs/remote/remote-overview) to
-get it set up.
-
-Suggest using VSCode's included settings sync so that your remote dev host has
-all the same app settings and extensions automagically.
-
-##### One remote dev gotcha
-
-I've found the automatic port forwarding to be very flakey. You can disable it
-in `Preferences: Open Remote Settings (ssh: hostname)`. Search for
-`remote.autoForwardPorts` and untick the box.
-
-To forward ports very reliably, use SSH on the remote dev client (e.g. your
-macbook). Here's how to forward both backend API port (`9090`) and the frontend
-live dev server port (`5173`):
-
-```bash
-ssh \
-    -L 9090:localhost:9090 \
-    -L 5173:localhost:5173 \
-    user@remote-dev-host
-```
-
-The forwarding stops when you close the terminal window, so suggest to do this
-_outside_ the VSCode integrated terminal in case you need to restart VSCode for
-an extension update or something
-
-Now, on your remote dev client, you can open `localhost:9090` and access the UI,
-now served from the remote dev host, just the same as if it was running on the
-client.

docs/features/CLI.md

@@ -6,51 +6,38 @@ title: Command-Line Interface
 
 ## **Interactive Command Line Interface**
 
-The InvokeAI command line interface (CLI) provides scriptable access
-to InvokeAI's features.Some advanced features are only available
-through the CLI, though they eventually find their way into the WebUI.
+The `invoke.py` script, located in `scripts/`, provides an interactive interface
+to image generation similar to the "invoke mothership" bot that Stable AI
+provided on its Discord server.
 
-The CLI is accessible from the `invoke.sh`/`invoke.bat` launcher by
-selecting option (1). Alternatively, it can be launched directly from
-the command line by activating the InvokeAI environment and giving the
-command:
-
-```bash
-invokeai
-```
-
-After some startup messages, you will be presented with the `invoke> `
-prompt. Here you can type prompts to generate images and issue other
-commands to load and manipulate generative models. The CLI has a large
-number of command-line options that control its behavior. To get a
-concise summary of the options, call `invokeai` with the `--help` argument:
-
-```bash
-invokeai --help
-```
+Unlike the `txt2img.py` and `img2img.py` scripts provided in the original
+[CompVis/stable-diffusion](https://github.com/CompVis/stable-diffusion) source
+code repository, the time-consuming initialization of the AI model
+initialization only happens once. After that image generation from the
+command-line interface is very fast.
 
 The script uses the readline library to allow for in-line editing, command
 history (++up++ and ++down++), autocompletion, and more. To help keep track of
 which prompts generated which images, the script writes a log file of image
 names and prompts to the selected output directory.
 
-Here is a typical session
+In addition, as of version 1.02, it also writes the prompt into the PNG file's
+metadata where it can be retrieved using `scripts/images2prompt.py`
+
+The script is confirmed to work on Linux, Windows and Mac systems.
+
+!!! note
+
+    This script runs from the command-line or can be used as a Web application. The Web GUI is
+    currently rudimentary, but a much better replacement is on its way.
 
 ```bash
-PS1:C:\Users\fred> invokeai
+(invokeai) ~/stable-diffusion$ python3 ./scripts/invoke.py
 * Initializing, be patient...
-* Initializing, be patient...
->> Initialization file /home/lstein/invokeai/invokeai.init found. Loading...
->> Internet connectivity is True
->> InvokeAI, version 2.3.0-rc5
->> InvokeAI runtime directory is "/home/lstein/invokeai"
->> GFPGAN Initialized
->> CodeFormer Initialized
->> ESRGAN Initialized
->> Using device_type cuda
->> xformers memory-efficient attention is available and enabled
-     (...more initialization messages...)
-* Initialization done! Awaiting your command (-h for help, 'q' to quit)
+Loading model from models/ldm/text2img-large/model.ckpt
+(...more initialization messages...)
+
+* Initialization done! Awaiting your command...
 invoke> ashley judd riding a camel -n2 -s150
 Outputs:
    outputs/img-samples/00009.png: "ashley judd riding a camel" -n2 -s150 -S 416354203
@@ -60,15 +47,27 @@ invoke> "there's a fly in my soup" -n6 -g
     outputs/img-samples/00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
     seeds for individual rows: [2685670268, 1216708065, 2335773498, 822223658, 714542046, 3395302430]
 invoke> q
+
+# this shows how to retrieve the prompt stored in the saved image's metadata
+(invokeai) ~/stable-diffusion$ python ./scripts/images2prompt.py outputs/img_samples/*.png
+00009.png: "ashley judd riding a camel" -s150 -S 416354203
+00010.png: "ashley judd riding a camel" -s150 -S 1362479620
+00011.png: "there's a fly in my soup" -n6 -g -S 2685670268
 ```
 
 ![invoke-py-demo](../assets/dream-py-demo.png)
 
+The `invoke>` prompt's arguments are pretty much identical to those used in the
+Discord bot, except you don't need to type `!invoke` (it doesn't hurt if you
+do). A significant change is that creation of individual images is now the
+default unless `--grid` (`-g`) is given. A full list is given in
+[List of prompt arguments](#list-of-prompt-arguments).
+
 ## Arguments
 
-The script recognizes a series of command-line switches that will
-change important global defaults, such as the directory for image
-outputs and the location of the model weight files.
+The script itself also recognizes a series of command-line switches that will
+change important global defaults, such as the directory for image outputs and
+the location of the model weight files.
 
 ### List of arguments recognized at the command line
 
@@ -83,14 +82,10 @@ overridden on a per-prompt basis (see
 | `--outdir <path>`                         | `-o<path>`                                | `outputs/img_samples`                          | Location for generated images.                                                                       |
 | `--prompt_as_dir`                         | `-p`                                      | `False`                                        | Name output directories using the prompt text.                                                       |
 | `--from_file <path>`                      |                                           | `None`                                         | Read list of prompts from a file. Use `-` to read from standard input                                |
-| `--model <modelname>`                     |                                           | `stable-diffusion-1.5`                         | Loads the initial model specified in configs/models.yaml. |
-| `--ckpt_convert `           |                                                         | `False`                                        | If provided both .ckpt and .safetensors files will be auto-converted into diffusers format in memory |
-| `--autoconvert <path>`                    |                          | `None`                                        | On startup, scan the indicated directory for new .ckpt/.safetensor files and automatically convert and import them |
-| `--precision`                             |                                           | `fp16`                                         | Provide `fp32` for full precision mode, `fp16` for half-precision. `fp32` needed for Macintoshes and some NVidia cards. |
+| `--model <modelname>`                     |                                           | `stable-diffusion-1.4`                         | Loads model specified in configs/models.yaml. Currently one of "stable-diffusion-1.4" or "laion400m" |
+| `--full_precision`                        | `-F`                                      | `False`                                        | Run in slower full-precision mode. Needed for Macintosh M1/M2 hardware and some older video cards.   |
 | `--png_compression <0-9>`                 | `-z<0-9>`                                 | `6`                                            | Select level of compression for output files, from 0 (no compression) to 9 (max compression)         |
 | `--safety-checker`                        |                                           | `False`                                        | Activate safety checker for NSFW and other potentially disturbing imagery                            |
-| `--patchmatch`, `--no-patchmatch`                 |                                   | `--patchmatch`                                        | Load/Don't load the PatchMatch inpainting extension    |
-| `--xformers`, `--no-xformers`                 |                                   | `--xformers`                                        | Load/Don't load the Xformers memory-efficient attention module (CUDA only)    |
 | `--web`                                   |                                           | `False`                                        | Start in web server mode                                                                             |
 | `--host <ip addr>`                        |                                           | `localhost`                                    | Which network interface web server should listen on. Set to 0.0.0.0 to listen on any.                |
 | `--port <port>`                           |                                           | `9090`                                         | Which port web server should listen for requests on.                                                 |
@@ -114,7 +109,6 @@ overridden on a per-prompt basis (see
 
     | Argument           |  Shortcut  |  Default            |  Description |
     |--------------------|------------|---------------------|--------------|
-    | `--full_precision`  |             | `False`                | Same as `--precision=fp32`|
     | `--weights <path>`   |            | `None`                | Path to weights file; use `--model stable-diffusion-1.4` instead |
     | `--laion400m`        | `-l`         | `False`               | Use older LAION400m weights; use `--model=laion400m` instead |
 
@@ -205,17 +199,15 @@ Here are the invoke> command that apply to txt2img:
 | `--seamless`                              |                                           | `False`                                  | Activate seamless tiling for interesting effects                                                                                                                                                                                                 |
 | `--seamless_axes`                         |                                           | `x,y`                                    | Specify which axes to use circular convolution on.                                                                                                                                                                                               |
 | `--log_tokenization`                      | `-t`                                      | `False`                                  | Display a color-coded list of the parsed tokens derived from the prompt                                                                                                                                                                          |
-| `--skip_normalization`                    | `-x`                                      | `False`                                  | Weighted subprompts will not be normalized. See [Weighted Prompts](../features/OTHER.md#weighted-prompts)                                                                                                                                                  |
+| `--skip_normalization`                    | `-x`                                      | `False`                                  | Weighted subprompts will not be normalized. See [Weighted Prompts](./OTHER.md#weighted-prompts)                                                                                                                                                  |
 | `--upscale <int> <float>`                 | `-U <int> <float>`                        | `-U 1 0.75`                              | Upscale image by magnification factor (2, 4), and set strength of upscaling (0.0-1.0). If strength not set, will default to 0.75.                                                                                                                |
 | `--facetool_strength <float>`             | `-G <float> `                             | `-G0`                                    | Fix faces (defaults to using the GFPGAN algorithm); argument indicates how hard the algorithm should try (0.0-1.0)                                                                                                                               |
 | `--facetool <name>`                       | `-ft <name>`                              | `-ft gfpgan`                             | Select face restoration algorithm to use: gfpgan, codeformer                                                                                                                                                                                     |
 | `--codeformer_fidelity`                   | `-cf <float>`                             | `0.75`                                   | Used along with CodeFormer. Takes values between 0 and 1. 0 produces high quality but low accuracy. 1 produces high accuracy but low quality                                                                                                     |
 | `--save_original`                         | `-save_orig`                              | `False`                                  | When upscaling or fixing faces, this will cause the original image to be saved rather than replaced.                                                                                                                                             |
-| `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](../features/VARIATIONS.md). |
-| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](../features/VARIATIONS.md) for now to use this.                                                                                                                                                           |
+| `--variation <float>`                     | `-v<float>`                               | `0.0`                                    | Add a bit of noise (0.0=none, 1.0=high) to the image in order to generate a series of variations. Usually used in combination with `-S<seed>` and `-n<int>` to generate a series a riffs on a starting image. See [Variations](./VARIATIONS.md). |
+| `--with_variations <pattern>`             |                                           | `None`                                   | Combine two or more variations. See [Variations](./VARIATIONS.md) for now to use this.                                                                                                                                                           |
 | `--save_intermediates <n>`                |                                           | `None`                                   | Save the image from every nth step into an "intermediates" folder inside the output directory                                                                                                                                                    |
-| `--h_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the X axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
-| `--v_symmetry_time_pct <float>`           |                                           | `None`                                   | Create symmetry along the Y axis at the desired percent complete of the generation process. (Must be between 0.0 and 1.0; set to a very small number like 0.0001 for just after the first step of generation.)                                   |
 
 !!! note
 
@@ -257,7 +249,7 @@ additional options:
     by `-M`. You may also supply just a single initial image with the areas
     to overpaint made transparent, but you must be careful not to destroy
     the pixels underneath when you create the transparent areas. See
-    [Inpainting](INPAINTING.md) for details.
+    [Inpainting](./INPAINTING.md) for details.
 
 inpainting accepts all the arguments used for txt2img and img2img, as well as
 the --mask (-M) and --text_mask (-tm) arguments:
@@ -297,7 +289,7 @@ invoke> a piece of cake -I /path/to/breakfast.png -tm bagel 0.6
 
 You can load and use hundreds of community-contributed Textual
 Inversion models just by typing the appropriate trigger phrase. Please
-see [Concepts Library](../features/CONCEPTS.md) for more details.
+see [Concepts Library](CONCEPTS.md) for more details.
 
 ## Other Commands
 
@@ -344,10 +336,8 @@ useful for debugging the text masking process prior to inpainting with the
 
 ### Model selection and importation
 
-The CLI allows you to add new models on the fly, as well as to switch
-among them rapidly without leaving the script. There are several
-different model formats, each described in the [Model Installation
-Guide](../installation/050_INSTALLING_MODELS.md).
+The CLI allows you to add new models on the fly, as well as to switch among them
+rapidly without leaving the script.
 
 #### `!models`
 
@@ -357,9 +347,9 @@ model is bold-faced
 Example:
 
 <pre>
-inpainting-1.5            not loaded  Stable Diffusion inpainting model
-<b>stable-diffusion-1.5          active  Stable Diffusion v1.5</b>
-waifu-diffusion           not loaded  Waifu Diffusion v1.4
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion           not loaded  Waifu Diffusion v1.3
 </pre>
 
 #### `!switch <model>`
@@ -371,30 +361,43 @@ Note how the second column of the `!models` table changes to `cached` after a
 model is first loaded, and that the long initialization step is not needed when
 loading a cached model.
 
-#### `!import_model <hugging_face_repo_ID>`
+<pre>
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          cached  Stable Diffusion v1.4</b>
+waifu-diffusion               active  Waifu Diffusion v1.3
 
-This imports and installs a `diffusers`-style model that is stored on
-the [HuggingFace Web Site](https://huggingface.co). You can look up
-any [Stable Diffusion diffusers
-model](https://huggingface.co/models?library=diffusers) and install it
-with a command like the following:
+invoke> !switch waifu-diffusion
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+>> Model loaded in 18.24s
+>> Max VRAM used to load the model: 2.17G
+>> Current VRAM usage:2.17G
+>> Setting Sampler to k_lms
 
-```bash
-!import_model prompthero/openjourney
-```
+invoke> !models
+laion400m                 not loaded  <no description>
+stable-diffusion-1.4          cached  Stable Diffusion v1.4
+<b>waifu-diffusion               active  Waifu Diffusion v1.3</b>
 
-#### `!import_model <path/to/diffusers/directory>`
+invoke> !switch stable-diffusion-1.4
+>> Caching model waifu-diffusion in system RAM
+>> Retrieving model stable-diffusion-1.4 from system RAM cache
+>> Setting Sampler to k_lms
 
-If you have a copy of a `diffusers`-style model saved to disk, you can
-import it by passing the path to model's top-level directory.
+invoke> !models
+laion400m                 not loaded  <no description>
+<b>stable-diffusion-1.4          active  Stable Diffusion v1.4</b>
+waifu-diffusion               cached  Waifu Diffusion v1.3
+</pre>
 
-#### `!import_model <url>`
-
-For a `.ckpt` or `.safetensors` file, if you have a direct download
-URL for the file, you can provide it to `!import_model` and the file
-will be downloaded and installed for you.
-
-#### `!import_model <path/to/model/weights.ckpt>`
+#### `!import_model <path/to/model/weights>`
 
 This command imports a new model weights file into InvokeAI, makes it available
 for image generation within the script, and writes out the configuration for the
@@ -414,12 +417,35 @@ below, the bold-faced text shows what the user typed in with the exception of
 the width, height and configuration file paths, which were filled in
 automatically.
 
-#### `!import_model <path/to/directory_of_models>`
+Example:
 
-If you provide the path of a directory that contains one or more
-`.ckpt` or `.safetensors` files, the CLI will scan the directory and
-interactively offer to import the models it finds there. Also see the
-`--autoconvert` command-line option.
+<pre>
+invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b>
+>> Model import in process. Please enter the values needed to configure this model:
+
+Name for this model: <b>waifu-diffusion</b>
+Description of this model: <b>Waifu Diffusion v1.3</b>
+Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b>
+Default image width: <b>512</b>
+Default image height: <b>512</b>
+>> New configuration:
+waifu-diffusion:
+  config: configs/stable-diffusion/v1-inference.yaml
+  description: Waifu Diffusion v1.3
+  height: 512
+  weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+  width: 512
+OK to import [n]? <b>y</b>
+>> Caching model stable-diffusion-1.4 in system RAM
+>> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt
+   | LatentDiffusion: Running in eps-prediction mode
+   | DiffusionWrapper has 859.52 M params.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+   | Making attention of type 'vanilla' with 512 in_channels
+   | Using faster float16 precision
+invoke>
+</pre>
 
 #### `!edit_model <name_of_model>`
 
@@ -453,6 +479,11 @@ OK to import [n]? y
 ...
 </pre>
 
+======= invoke> !fix 000017.4829112.gfpgan-00.png --embiggen 3 ...lots of
+text... Outputs: [2] outputs/img-samples/000018.2273800735.embiggen-00.png: !fix
+"outputs/img-samples/000017.243781548.gfpgan-00.png" -s 50 -S 2273800735 -W 512
+-H 512 -C 7.5 -A k_lms --embiggen 3.0 0.75 0.25 ```
+
 ### History processing
 
 The CLI provides a series of convenient commands for reviewing previous actions,

docs/features/CONCEPTS.md

@@ -1,12 +1,9 @@
 ---
-title: Concepts 
+title: Concepts Library
 ---
 
 # :material-library-shelves: The Hugging Face Concepts Library and Importing Textual Inversion files
 
-With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model. 
-
-
 ## Using Textual Inversion Files
 
 Textual inversion (TI) files are small models that customize the output of
@@ -15,16 +12,18 @@ and artistic styles. They are also known as "embeds" in the machine learning
 world.
 
 Each TI file introduces one or more vocabulary terms to the SD model. These are
-known in InvokeAI as "triggers." Triggers are denoted using angle brackets 
-as in "<trigger-phrase>". The two most common type of
+known in InvokeAI as "triggers." Triggers are often, but not always, denoted
+using angle brackets as in "<trigger-phrase>". The two most common type of
 TI files that you'll encounter are `.pt` and `.bin` files, which are produced by
 different TI training packages. InvokeAI supports both formats, but its
-[built-in TI training system](TRAINING.md) produces `.pt`.
+[built-in TI training system](TEXTUAL_INVERSION.md) produces `.pt`.
 
 The [Hugging Face company](https://huggingface.co/sd-concepts-library) has
 amassed a large ligrary of >800 community-contributed TI files covering a
-broad range of subjects and styles. You can also install your own or others' TI files 
-by placing them in the designated directory for the compatible model type
+broad range of subjects and styles. InvokeAI has built-in support for this
+library which downloads and merges TI files automatically upon request. You can
+also install your own or others' TI files by placing them in a designated
+directory.
 
 ### An Example
 
@@ -42,43 +41,91 @@ You can also combine styles and concepts:
   | :--------------------------------------------------------: |
   | ![](../assets/concepts/image5.png)                         |
 </figure>
+## Using a Hugging Face Concept
 
+!!! warning "Authenticating to HuggingFace"
+
+    Some concepts require valid authentication to HuggingFace. Without it, they will not be downloaded
+    and will be silently ignored.
+
+    If you used an installer to install InvokeAI, you may have already set a HuggingFace token.
+    If you skipped this step, you can:
+
+    - run the InvokeAI configuration script again (if you used a manual installer): `invokeai-configure`
+    - set one of the `HUGGINGFACE_TOKEN` or `HUGGING_FACE_HUB_TOKEN` environment variables to contain your token
+
+    Finally, if you already used any HuggingFace library on your computer, you might already have a token
+    in your local cache. Check for a hidden `.huggingface` directory in your home folder. If it
+    contains a `token` file, then you are all set.
+
+
+Hugging Face TI concepts are downloaded and installed automatically as you
+require them. This requires your machine to be connected to the Internet. To
+find out what each concept is for, you can browse the
+[Hugging Face concepts library](https://huggingface.co/sd-concepts-library) and
+look at examples of what each concept produces.
+
+When you have an idea of a concept you wish to try, go to the command-line
+client (CLI) and type a `<` character and the beginning of the Hugging Face
+concept name you wish to load. Press ++tab++, and the CLI will show you all
+matching concepts. You can also type `<` and hit ++tab++ to get a listing of all
+~800 concepts, but be prepared to scroll up to see them all! If there is more
+than one match you can continue to type and ++tab++ until the concept is
+completed.
+
+!!! example
+
+    if you type in `<x` and hit ++tab++, you'll be prompted with the completions:
+
+    ```py
+    <xatu2>        <xatu>         <xbh>          <xi>           <xidiversity>  <xioboma>      <xuna>         <xyz>
+    ```
+
+    Now type `id` and press ++tab++. It will be autocompleted to `<xidiversity>`
+    because this is a unique match.
+
+    Finish your prompt and generate as usual. You may include multiple concept terms
+    in the prompt.
+
+If you have never used this concept before, you will see a message that the TI
+model is being downloaded and installed. After this, the concept will be saved
+locally (in the `models/sd-concepts-library` directory) for future use.
+
+Several steps happen during downloading and installation, including a scan of
+the file for malicious code. Should any errors occur, you will be warned and the
+concept will fail to load. Generation will then continue treating the trigger
+term as a normal string of characters (e.g. as literal `<ghibli-face>`).
+
+You can also use `<concept-names>` in the WebGUI's prompt textbox. There is no
+autocompletion at this time.
 
 ## Installing your Own TI Files
 
 You may install any number of `.pt` and `.bin` files simply by copying them into
-the `embedding` directory of the corresponding InvokeAI models directory (usually `invokeai`
-in your home directory). For example, you can simply move a Stable Diffusion 1.5 embedding file to
-the `sd-1/embedding` folder. Be careful not to overwrite one file with another.
+the `embeddings` directory of the InvokeAI runtime directory (usually `invokeai`
+in your home directory). You may create subdirectories in order to organize the
+files in any way you wish. Be careful not to overwrite one file with another.
 For example, TI files generated by the Hugging Face toolkit share the named
-`learned_embedding.bin`. You can rename these, or use subdirectories to keep them distinct.
+`learned_embedding.bin`. You can use subdirectories to keep them distinct.
 
-At startup time, InvokeAI will scan the various `embedding` directories and load any TI
-files it finds there for compatible models. At startup you will see a message similar to this one:
+At startup time, InvokeAI will scan the `embeddings` directory and load any TI
+files it finds there. At startup you will see a message similar to this one:
 
 ```bash
->> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
+>> Current embedding manager terms: *, <HOI4-Leader>, <princess-knight>
 ```
-To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and 
-select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
 
-## Using LoRAs
+Note the `*` trigger term. This is a placeholder term that many early TI
+tutorials taught people to use rather than a more descriptive term.
+Unfortunately, if you have multiple TI files that all use this term, only the
+first one loaded will be triggered by use of the term.
 
-LoRA files are models that customize the output of Stable Diffusion image generation.
-Larger than embeddings, but much smaller than full models, they augment SD with improved
-understanding of subjects and artistic styles.
+To avoid this problem, you can use the `merge_embeddings.py` script to merge two
+or more TI files together. If it encounters a collision of terms, the script
+will prompt you to select new terms that do not collide. See
+[Textual Inversion](TEXTUAL_INVERSION.md) for details.
 
-Unlike TI files, LoRAs do not introduce novel vocabulary into the model's known tokens. Instead,
-LoRAs augment the model's weights that are applied to generate imagery. LoRAs may be supplied
-with a "trigger" word that they have been explicitly trained on, or may simply apply their 
-effect without being triggered.
-
-LoRAs are typically stored in .safetensors files, which are the most secure way to store and transmit
-these types of weights. You may install any number of `.safetensors` LoRA files simply by copying them into
-the `lora` directory of the corresponding InvokeAI models directory (usually `invokeai`
-in your home directory). For example, you can simply move a Stable Diffusion 1.5 LoRA file to
-the `sd-1/lora` folder.
-
-To use these when generating, open the LoRA menu item in the options panel, select the LoRAs you want to apply
-and ensure that they have the appropriate weight recommended by the model provider. Typically, most LoRAs perform best at a weight of .75-1. 
+## Further Reading
 
+Please see [the repository](https://github.com/rinongal/textual_inversion) and
+associated paper for details and limitations.

docs/features/CONFIGURATION.md

@@ -1,287 +0,0 @@
----
-title: Configuration
----
-
-# :material-tune-variant: InvokeAI Configuration
-
-## Intro
-
-InvokeAI has numerous runtime settings which can be used to adjust
-many aspects of its operations, including the location of files and
-directories, memory usage, and performance. These settings can be
-viewed and customized in several ways:
-
-1. By editing settings in the `invokeai.yaml` file.
-2. By setting environment variables.
-3. On the command-line, when InvokeAI is launched.
-
-In addition, the most commonly changed settings are accessible
-graphically via the `invokeai-configure` script.
-
-### How the Configuration System Works
-
-When InvokeAI is launched, the very first thing it needs to do is to
-find its "root" directory, which contains its configuration files,
-installed models, its database of images, and the folder(s) of
-generated images themselves. In this document, the root directory will
-be referred to as ROOT.
-
-#### Finding the Root Directory
-
-To find its root directory, InvokeAI uses the following recipe:
-
-1. It first looks for the argument `--root <path>` on the command line
-it was launched from, and uses the indicated path if present.
-
-2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
-the directory path found there if present.
-
-3. If neither of these are present, then InvokeAI looks for the
-folder containing the `.venv` Python virtual environment directory for
-the currently active environment. This directory is checked for files
-expected inside the InvokeAI root before it is used.
-
-4. Finally, InvokeAI looks for a directory in the current user's home
-directory named `invokeai`.
-
-#### Reading the InvokeAI Configuration File
-
-Once the root directory has been located, InvokeAI looks for a file
-named `ROOT/invokeai.yaml`, and if present reads configuration values
-from it. The top of this file looks like this:
-
-```
-InvokeAI:
-  Web Server:
-    host: localhost
-    port: 9090
-    allow_origins: []
-    allow_credentials: true
-    allow_methods:
-    - '*'
-    allow_headers:
-    - '*'
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    nsfw_checker: false
-    patchmatch: true
-    restore: true
-...
-```
-
-This lines in this file are used to establish default values for
-Invoke's settings. In the above fragment, the Web Server's listening
-port is set to 9090 by the `port` setting.
-
-You can edit this file with a text editor such as "Notepad" (do not
-use Word or any other word processor). When editing, be careful to
-maintain the indentation, and do not add extraneous text, as syntax
-errors will prevent InvokeAI from launching. A basic guide to the
-format of YAML files can be found
-[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
-
-You can fix a broken `invokeai.yaml` by deleting it and running the
-configuration script again -- option [7] in the launcher, "Re-run the
-configure script".
-
-#### Reading Environment Variables
-
-Next InvokeAI looks for defined environment variables in the format
-`INVOKEAI_<setting_name>`, for example `INVOKEAI_port`. Environment
-variable values take precedence over configuration file variables. On
-a Macintosh system, for example, you could change the port that the
-web server listens on by setting the environment variable this way:
-
-```
-export INVOKEAI_port=8000
-invokeai-web
-```
-
-Please check out these
-[Macintosh](https://phoenixnap.com/kb/set-environment-variable-mac)
-and
-[Windows](https://phoenixnap.com/kb/windows-set-environment-variable)
-guides for setting temporary and permanent environment variables.
-
-#### Reading the Command Line
-
-Lastly, InvokeAI takes settings from the command line, which override
-everything else. The command-line settings have the same name as the
-corresponding configuration file settings, preceded by a `--`, for
-example `--port 8000`.
-
-If you are using the launcher (`invoke.sh` or `invoke.bat`) to launch
-InvokeAI, then just pass the command-line arguments to the launcher:
-
-```
-invoke.bat --port 8000 --host 0.0.0.0
-```
-
-The arguments will be applied when you select the web server option
-(and the other options as well).
-
-If, on the other hand, you prefer to launch InvokeAI directly from the
-command line, you would first activate the virtual environment (known
-as the "developer's console" in the launcher), and run `invokeai-web`:
-
-```
-> C:\Users\Fred\invokeai\.venv\scripts\activate
-(.venv) > invokeai-web --port 8000 --host 0.0.0.0
-```
-
-You can get a listing and brief instructions for each of the
-command-line options by giving the `--help` argument:
-
-```
-(.venv) > invokeai-web --help
-usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials]
-                [--allow_methods [ALLOW_METHODS ...]] [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan]
-                [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
-                [--nsfw_checker | --no-nsfw_checker] [--patchmatch | --no-patchmatch] [--restore | --no-restore]
-                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_cache_size MAX_CACHE_SIZE]
-                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--precision {auto,float16,float32,autocast}]
-                [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled]
-                [--tiled_decode | --no-tiled_decode] [--root ROOT] [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR]
-                [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH] [--models_dir MODELS_DIR]
-                [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
-                [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]]
-                [--log_format {plain,color,syslog,legacy}] [--log_level {debug,info,warning,error,critical}]
-...
-```
-
-## The Configuration Settings
-
-The configuration settings are divided into several distinct
-groups in `invokeia.yaml`:
-
-### Web Server
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `host`     | `localhost`      | Name or IP address of the network interface that the web server will listen on  |
-| `port`     | `9090`           | Network port number that the web server will listen on  |
-| `allow_origins`  | `[]`       | A list of host names or IP addresses that are allowed to connect to the InvokeAI API in the format `['host1','host2',...]` |
-| `allow_credentials | `true`   | Require credentials for a foreign host to access the InvokeAI API (don't change this) |
-| `allow_methods` | `*`         | List of HTTP methods ("GET", "POST") that the web server is allowed to use when accessing the API  |
-| `allow_headers` | `*`         | List of HTTP headers that the web server will accept when accessing the API  |
-
-The documentation for InvokeAI's API can be accessed by browsing to the following URL: [http://localhost:9090/docs].
-
-### Features
-
-These configuration settings allow you to enable and disable various InvokeAI features:
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `esrgan`     | `true`      | Activate the ESRGAN upscaling options|
-| `internet_available` | `true`     | When a resource is not available locally, try to fetch it via the internet |
-| `log_tokenization` | `false`      | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
-| `nsfw_checker` | `true`     | Activate the NSFW checker to blur out risque images |
-| `patchmatch` | `true`     | Activate the "patchmatch" algorithm for improved inpainting |
-| `restore`    | `true`     | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
-
-### Memory/Performance
-
-These options tune InvokeAI's memory and performance characteristics.
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `always_use_cpu`     | `false`      | Use the CPU to generate images, even if a GPU is available |
-| `free_gpu_mem`       | `false`      | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
-| `max_cache_size`       | `6`      | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
-| `max_vram_cache_size`  | `2.75`   | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
-| `precision`       | `auto`      | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
-| `sequential_guidance`     | `false`      | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
-| `xformers_enabled`        | `true`      | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
-| `tiled_decode`            | `false`     | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
-
-### Paths
-
-These options set the paths of various directories and files used by
-InvokeAI. Relative paths are interpreted relative to INVOKEAI_ROOT, so
-if INVOKEAI_ROOT is `/home/fred/invokeai` and the path is
-`autoimport/main`, then the corresponding directory will be located at
-`/home/fred/invokeai/autoimport/main`.
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `autoimport_dir` | `autoimport/main`     | At startup time, read and import any main model files found in this directory |
-| `lora_dir` | `autoimport/lora`     | At startup time, read and import any LoRA/LyCORIS models found in this directory |
-| `embedding_dir` | `autoimport/embedding`  | At startup time, read and import any textual inversion (embedding) models found in this directory |
-| `controlnet_dir` | `autoimport/controlnet`  | At startup time, read and import any ControlNet models found in this directory |
-| `conf_path` | `configs/models.yaml`  | Location of the `models.yaml` model configuration file |
-| `models_dir` | `models`  | Location of the directory containing models installed by InvokeAI's model manager |
-| `legacy_conf_dir` | `configs/stable-diffusion`  | Location of the directory containing the .yaml configuration files for legacy checkpoint models |
-| `db_dir` | `databases`  | Location of the directory containing InvokeAI's image, schema and session database |
-| `outdir` | `outputs`  | Location of the directory in which the gallery of generated and uploaded images will be stored |
-| `use_memory_db` | `false`  | Keep database information in memory rather than on disk; this will not preserve image gallery information across restarts |
-
-Note that the autoimport directories will be searched recursively,
-allowing you to organize the models into folders and subfolders in any
-way you wish. In addition, while we have split up autoimport
-directories by the type of model they contain, this isn't
-necessary. You can combine different model types in the same folder
-and InvokeAI will figure out what they are. So you can easily use just
-one autoimport directory by commenting out the unneeded paths:
-
-```
-Paths:
-  autoimport_dir: autoimport
-#  lora_dir: null
-#  embedding_dir: null
-#  controlnet_dir: null
-```
-
-### Logging
-
-These settings control the information, warning, and debugging
-messages printed to the console log while InvokeAI is running:
-
-| Setting  | Default Value  |  Description |
-|----------|----------------|--------------|
-| `log_handlers` | `console` | This controls where log messages are sent, and can be a list of one or more destinations. Values include `console`, `file`, `syslog` and `http`. These are described in more detail below |
-| `log_format` | `color` | This controls the formatting of the log messages. Values are `plain`, `color`, `legacy` and `syslog` |
-| `log_level`  | `debug` | This filters messages according to the level of severity and can be one of `debug`, `info`, `warning`, `error` and `critical`. For example, setting to `warning` will display all messages at the warning level or higher, but won't display "debug" or "info" messages |
-
-Several different log handler destinations are available, and multiple destinations are supported by providing a list:
-
-```
-  log_handlers:
-     - console
-     - syslog=localhost
-     - file=/var/log/invokeai.log
-```
-
-* `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
-
-* `syslog` is only available on Linux and Macintosh systems. It uses
-  the operating system's "syslog" facility to write log file entries
-  locally or to a remote logging machine. `syslog` offers a variety
-  of configuration options:
-
-```
-  syslog=/dev/log`      - log to the /dev/log device
-  syslog=localhost`     - log to the network logger running on the local machine
-  syslog=localhost:512` - same as above, but using a non-standard port
-  syslog=fredserver,facility=LOG_USER,socktype=SOCK_DRAM`
-                        - Log to LAN-connected server "fredserver" using the facility LOG_USER and datagram packets.
-```
-
-* `http` can be used to log to a remote web server. The server must be
-  properly configured to receive and act on log messages. The option
-  accepts the URL to the web server, and a `method` argument
-  indicating whether the message should be submitted using the GET or
-  POST method.
-
-```
- http=http://my.server/path/to/logger,method=POST
-```
-
-The `log_format` option provides several alternative formats:
-
-* `color`    - default format providing time, date and a message, using text colors to distinguish different log severities
-* `plain`    - same as above, but monochrome text only
-* `syslog`   - the log level and error message only, allowing the syslog system to attach the time and date
-* `legacy`   - a format similar to the one used by the legacy 2.3 InvokeAI releases.

docs/features/CONTROLNET.md

@@ -1,92 +0,0 @@
----
-title: ControlNet
----
-
-# :material-loupe: ControlNet
-
-## ControlNet
-
-ControlNet
-
-ControlNet is a powerful set of features developed by the open-source community (notably, Stanford researcher [**@ilyasviel**](https://github.com/lllyasviel)) that allows you to apply a secondary neural network model to your image generation process in Invoke.
-
-With ControlNet, you can get more control over the output of your image generation, providing you with a way to direct the network towards generating images that better fit your desired style or outcome.
-
-
-### How it works
-
-ControlNet works by analyzing an input image, pre-processing that image to identify relevant information that can be interpreted by each specific ControlNet model, and then inserting that control information into the generation process. This can be used to adjust the style, composition, or other aspects of the image to better achieve a specific result.
-
-
-### Models
-
-As part of the model installation, ControlNet models can be selected including a variety of pre-trained models that have been added to achieve different effects or styles in your generated images. Further ControlNet models may require additional code functionality to also be incorporated into Invoke's Invocations folder. You should expect to follow any installation instructions for ControlNet models loaded  outside the default models provided by Invoke. The default models include:
-
-
-**Canny**:
-
-When the Canny model is used in ControlNet, Invoke will attempt to generate images that match the edges detected. 
-
-Canny edge detection works by detecting the edges in an image by looking for abrupt changes in intensity. It is known for its ability to detect edges accurately while reducing noise and false edges, and the preprocessor can identify more information by decreasing the thresholds.
-
-**M-LSD**: 
-
-M-LSD is another edge detection algorithm used in ControlNet. It stands for Multi-Scale Line Segment Detector. 
-
-It detects straight line segments in an image by analyzing the local structure of the image at multiple scales.  It can be useful for architectural imagery, or anything where straight-line structural information is needed for the resulting output. 
-
-**Lineart**: 
-
-The Lineart model in ControlNet generates line drawings from an input image. The resulting pre-processed image is a simplified version of the original, with only the outlines of objects visible.The Lineart model in ControlNet is known for its ability to accurately capture the contours of the objects in an input sketch. 
-
-**Lineart Anime**: 
-
-A variant of the Lineart model that generates line drawings with a distinct style inspired by anime and manga art styles.
-
-**Depth**: 
-A model that generates depth maps of images, allowing you to create more realistic 3D models or to simulate depth effects in post-processing.
-
-**Normal Map (BAE):** 
-A model that generates normal maps from input images, allowing for more realistic lighting effects in 3D rendering.
-		
-**Image Segmentation**: 
-A model that divides input images into segments or regions, each of which corresponds to a different object or part of the image. (More details coming soon)
-
-
-**Openpose**: 
-The OpenPose control model allows for the identification of the general pose of a character by pre-processing an existing image with a clear human structure. With advanced options, Openpose can also detect the face or hands in the image. 
-
-**Mediapipe Face**:
-
-The MediaPipe Face identification processor is able to clearly identify facial features in order to capture vivid expressions of human faces.
-
-**Tile (experimental)**:
-
-The Tile model fills out details in the image to match the image, rather than the prompt. The Tile Model is a versatile tool that offers a range of functionalities. Its primary capabilities can be boiled down to two main behaviors:
-
-- It can reinterpret specific details within an image and create fresh, new elements.
-- It has the ability to disregard global instructions if there's a discrepancy between them and the local context or specific parts of the image. In such cases, it uses the local context to guide the process.
-
-The Tile Model can be a powerful tool in your arsenal for enhancing image quality and details. If there are undesirable elements in your images, such as blurriness caused by resizing, this model can effectively eliminate these issues, resulting in cleaner, crisper images. Moreover, it can generate and add refined details to your images, improving their overall quality and appeal. 
-
-**Pix2Pix (experimental)**
-
-With Pix2Pix, you can input an image into the controlnet, and then "instruct" the model to change it using your prompt. For example, you can say "Make it winter" to add more wintry elements to a scene.
-
-**Inpaint**: Coming Soon - Currently this model is available but not functional on the Canvas. An upcoming release will provide additional capabilities for using this model when inpainting.
-
-Each of these models can be adjusted and combined with other ControlNet models to achieve different results, giving you even more control over your image generation process.
-
-
-## Using ControlNet
-
-To use ControlNet, you can simply select the desired model and adjust both the ControlNet and Pre-processor settings to achieve the desired result. You can also use multiple ControlNet models at the same time, allowing you to achieve even more complex effects or styles in your generated images.
-
-
-Each ControlNet has two settings that are applied to the ControlNet.
-
-Weight - Strength of the Controlnet model applied to the generation for the section, defined by start/end.
-
-Start/End  - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
-
-Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.

docs/features/IMG2IMG.md

@@ -4,13 +4,75 @@ title: Image-to-Image
 
 # :material-image-multiple: Image-to-Image
 
-InvokeAI provides an "img2img" feature that lets you seed your
-creations with an initial drawing or photo. This is a really cool
-feature that tells stable diffusion to build the prompt on top of the
-image you provide, preserving the original's basic shape and layout.
+## `img2img`
 
-For a walkthrough of using Image-to-Image in the Web UI, see [InvokeAI
-Web Server](./WEB.md#image-to-image).
+This script also provides an `img2img` feature that lets you seed your creations
+with an initial drawing or photo. This is a really cool feature that tells
+stable diffusion to build the prompt on top of the image you provide, preserving
+the original's basic shape and layout. To use it, provide the `--init_img`
+option as shown here:
+
+!!! example ""
+
+    ```commandline
+    tree on a hill with a river, nature photograph, national geographic -I./test-pictures/tree-and-river-sketch.png -f 0.85
+    ```
+
+    <figure markdown>
+
+    | original image | generated image |
+    | :------------: | :-------------: |
+    | ![original-image](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png){ width=320 } | ![generated-image](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png){ width=320 } |
+
+    </figure>
+
+The `--init_img` (`-I`) option gives the path to the seed picture. `--strength`
+(`-f`) controls how much the original will be modified, ranging from `0.0` (keep
+the original intact), to `1.0` (ignore the original completely). The default is
+`0.75`, and ranges from `0.25-0.90` give interesting results. Other relevant
+options include `-C` (classification free guidance scale), and `-s` (steps).
+Unlike `txt2img`, adding steps will continuously change the resulting image and
+it will not converge.
+
+You may also pass a `-v<variation_amount>` option to generate `-n<iterations>`
+count variants on the original image. This is done by passing the first
+generated image back into img2img the requested number of times. It generates
+interesting variants.
+
+Note that the prompt makes a big difference. For example, this slight variation
+on the prompt produces a very different image:
+
+<figure markdown>
+![](https://user-images.githubusercontent.com/111189/194135220-16b62181-b60c-4248-8989-4834a8fd7fbd.png){ width=320 }
+<caption markdown>photograph of a tree on a hill with a river</caption>
+</figure>
+
+!!! tip
+
+    When designing prompts, think about how the images scraped from the internet were
+    captioned. Very few photographs will be labeled "photograph" or "photorealistic."
+    They will, however, be captioned with the publication, photographer, camera model,
+    or film settings.
+
+If the initial image contains transparent regions, then Stable Diffusion will
+only draw within the transparent regions, a process called
+[`inpainting`](./INPAINTING.md#creating-transparent-regions-for-inpainting).
+However, for this to work correctly, the color information underneath the
+transparent needs to be preserved, not erased.
+
+!!! warning "**IMPORTANT ISSUE** "
+
+    `img2img` does not work properly on initial images smaller
+    than 512x512. Please scale your image to at least 512x512 before using it.
+    Larger images are not a problem, but may run out of VRAM on your GPU card. To
+    fix this, use the --fit option, which downscales the initial image to fit within
+    the box specified by width x height:
+
+    ```
+    tree on a hill with a river, national geographic -I./test-pictures/big-sketch.png -H512 -W512 --fit
+    ```
+
+## How does it actually work, though?
 
 The main difference between `img2img` and `prompt2img` is the starting point.
 While `prompt2img` always starts with pure gaussian noise and progressively
@@ -26,6 +88,10 @@ seed `1592514025` develops something like this:
 
 !!! example ""
 
+    ```bash
+    invoke> "fire" -s10 -W384 -H384 -S1592514025
+    ```
+
     <figure markdown>
     ![latent steps](../assets/img2img/000019.steps.png){ width=720 }
     </figure>
@@ -80,8 +146,17 @@ Diffusion has less chance to refine itself, so the result ends up inheriting all
 the problems of my bad drawing.
 
 If you want to try this out yourself, all of these are using a seed of
-`1592514025` with a width/height of `384`, step count `10`, the
-`k_lms` sampler, and the single-word prompt `"fire"`.
+`1592514025` with a width/height of `384`, step count `10`, the default sampler
+(`k_lms`), and the single-word prompt `"fire"`:
+
+```bash
+invoke> "fire" -s10 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png --strength 0.7
+```
+
+The code for rendering intermediates is on my (damian0815's) branch
+[document-img2img](https://github.com/damian0815/InvokeAI/tree/document-img2img) -
+run `invoke.py` and check your `outputs/img-samples/intermediates` folder while
+generating an image.
 
 ### Compensating for the reduced step count
 
@@ -94,6 +169,10 @@ give each generation 20 steps.
 Here's strength `0.4` (note step count `50`, which is `20 ÷ 0.4` to make sure SD
 does `20` steps from my image):
 
+```bash
+invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
+```
+
 <figure markdown>
 ![000035.1592514025](../assets/img2img/000035.1592514025.png)
 </figure>
@@ -101,6 +180,10 @@ does `20` steps from my image):
 and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to
 make sure SD does `20` steps from my image):
 
+```commandline
+invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
+```
+
 <figure markdown>
 ![000046.1592514025](../assets/img2img/000046.1592514025.png)
 </figure>

docs/features/INPAINTING.md

@@ -168,15 +168,11 @@ used by Stable Diffusion 1.4 and 1.5.
 After installation, your `models.yaml` should contain an entry that looks like
 this one:
 
-```yml
-inpainting-1.5:
-  weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
-  description: SD inpainting v1.5
-  config: configs/stable-diffusion/v1-inpainting-inference.yaml
-  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
-  width: 512
-  height: 512
-```
+inpainting-1.5: weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+description: SD inpainting v1.5 config:
+configs/stable-diffusion/v1-inpainting-inference.yaml vae:
+models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt width: 512
+height: 512
 
 As shown in the example, you may include a VAE fine-tuning weights file as well.
 This is strongly recommended.

docs/features/LOGGING.md

@@ -1,171 +0,0 @@
----
-title: Controlling Logging
----
-
-# :material-image-off: Controlling Logging
-
-## Controlling How InvokeAI Logs Status Messages
-
-InvokeAI logs status messages using a configurable logging system. You
-can log to the terminal window, to a designated file on the local
-machine, to the syslog facility on a Linux or Mac, or to a properly
-configured web server. You can configure several logs at the same
-time, and control the level of message logged and the logging format
-(to a limited extent).
-
-Three command-line options control logging:
-
-### `--log_handlers <handler1> <handler2> ...`
-
-This option activates one or more log handlers. Options are "console",
-"file", "syslog" and "http". To specify more than one, separate them
-by spaces:
-
-```bash
-invokeai-web --log_handlers console syslog=/dev/log file=C:\Users\fred\invokeai.log
-```
-
-The format of these options is described below.
-
-### `--log_format {plain|color|legacy|syslog}`
-
-This controls the format of log messages written to the console. Only
-the "console" log handler is currently affected by this setting.
-
-* "plain" provides formatted messages like this:
-
-```bash
-
-[2023-05-24 23:18:2[2023-05-24 23:18:50,352]::[InvokeAI]::DEBUG --> this is a debug message
-[2023-05-24 23:18:50,352]::[InvokeAI]::INFO --> this is an informational messages
-[2023-05-24 23:18:50,352]::[InvokeAI]::WARNING --> this is a warning
-[2023-05-24 23:18:50,352]::[InvokeAI]::ERROR --> this is an error
-[2023-05-24 23:18:50,352]::[InvokeAI]::CRITICAL --> this is a critical error
-```
-
-* "color" produces similar output, but the text will be color coded to
-indicate the severity of the message.
-
-* "legacy" produces output similar to InvokeAI versions 2.3 and earlier:
-
-```bash
-### this is a critical error
-*** this is an error
-** this is a warning
->> this is an informational messages
-   | this is a debug message
-```
-
-* "syslog" produces messages suitable for syslog entries:
-
-```bash
-InvokeAI [2691178] <CRITICAL> this is a critical error
-InvokeAI [2691178] <ERROR> this is an error
-InvokeAI [2691178] <WARNING> this is a warning
-InvokeAI [2691178] <INFO> this is an informational messages
-InvokeAI [2691178] <DEBUG> this is a debug message
-```
-
-(note that the date, time and hostname will be added by the syslog
-system)
-
-### `--log_level {debug|info|warning|error|critical}`
-
-Providing this command-line option will cause only messages at the
-specified level or above to be emitted.
-
-## Console logging
-
-When "console" is provided to `--log_handlers`, messages will be
-written to the command line window in which InvokeAI was launched. By
-default, the color formatter will be used unless overridden by
-`--log_format`.
-
-## File logging
-
-When "file" is provided to `--log_handlers`, entries will be written
-to the file indicated in the path argument. By default, the "plain"
-format will be used:
-
-```bash
-invokeai-web --log_handlers file=/var/log/invokeai.log
-```
-
-## Syslog logging
-
-When "syslog" is requested, entries will be sent to the syslog
-system. There are a variety of ways to control where the log message
-is sent:
-
-* Send to the local machine using the `/dev/log` socket:
-
-```
-invokeai-web --log_handlers syslog=/dev/log
-```
-
-* Send to the local machine using a UDP message:
-
-```
-invokeai-web --log_handlers syslog=localhost
-```
-
-* Send to the local machine using a UDP message on a nonstandard
-  port:
-
-```
-invokeai-web --log_handlers syslog=localhost:512
-```
-
-* Send to a remote machine named "loghost" on the local LAN using
-  facility LOG_USER and UDP packets:
-
-```
-invokeai-web --log_handlers syslog=loghost,facility=LOG_USER,socktype=SOCK_DGRAM
-```
-
-This can be abbreviated `syslog=loghost`, as LOG_USER and SOCK_DGRAM
-are defaults.
-
-* Send to a remote machine named "loghost" using the facility LOCAL0
-  and using a TCP socket:
-
-```
-invokeai-web --log_handlers syslog=loghost,facility=LOG_LOCAL0,socktype=SOCK_STREAM
-```
-
-If no arguments are specified (just a bare "syslog"), then the logging
-system will look for a UNIX socket named `/dev/log`, and if not found
-try to send a UDP message to `localhost`. The Macintosh OS used to
-support logging to a socket named `/var/run/syslog`, but this feature
-has since been disabled.
-
-## Web logging
-
-If you have access to a web server that is configured to log messages
-when a particular URL is requested, you can log using the "http"
-method:
-
-```
-invokeai-web --log_handlers http=http://my.server/path/to/logger,method=POST
-```
-
-The optional [,method=] part can be used to specify whether the URL
-accepts GET (default) or POST messages.
-
-Currently password authentication and SSL are not supported.
-
-## Using the configuration file
-
-You can set and forget logging options by adding a "Logging" section
-to `invokeai.yaml`:
-
-```
-InvokeAI:
-  [... other settings...]
-  Logging:
-    log_handlers:
-       - console
-       - syslog=/dev/log
-    log_level: info
-    log_format: color
-```

docs/features/MODEL_MERGING.md

@@ -71,3 +71,6 @@ under the selected name and register it with InvokeAI.
   use InvokeAI conventions - only alphanumeric letters and the
   characters ".+-".
 
+## Caveats
+
+This is a new script and may contain bugs.

docs/features/NODES.md

@@ -1,206 +0,0 @@
-# Nodes Editor (Experimental)
-
-🚨
-*The node editor is experimental. We've made it accessible because we use it to develop the application, but we have not addressed the many known rough edges. It's very easy to shoot yourself in the foot, and we cannot offer support for it until it sees full release (ETA v3.1). Everything is subject to change without warning.* 
-🚨
-
-The nodes editor is a blank canvas allowing for the use of individual functions and image transformations to control the image generation workflow. The node processing flow is usually done from left (inputs) to right (outputs), though linearity can become abstracted the more complex the node graph becomes. Nodes inputs and outputs are connected by dragging connectors from node to node.
-
-To better understand how nodes are used, think of how an electric power bar works. It takes in one input (electricity from a wall outlet) and passes it to multiple devices through multiple outputs. Similarly, a node could have multiple inputs and outputs functioning at the same (or different) time, but all node outputs pass information onward like a power bar passes electricity. Not all outputs are compatible with all inputs, however - Each node has different constraints on how it is expecting to input/output information. In general, node outputs are colour-coded to match compatible inputs of other nodes.
-
-## Anatomy of a Node
-
-Individual nodes are made up of the following:
-
-- Inputs: Edge points on the left side of the node window where you connect outputs from other nodes.
-- Outputs: Edge points on the right side of the node window where you connect to inputs on other nodes.
-- Options: Various options which are either manually configured, or overridden by connecting an output from another node to the input.
-
-## Diffusion Overview
-
-Taking the time to understand the diffusion process will help you to understand how to set up your nodes in the nodes editor. 
-
-There are two main spaces Stable Diffusion works in: image space and latent space.
-
-Image space represents images in pixel form that you look at. Latent space represents compressed inputs. It’s in latent space that Stable Diffusion processes images. A VAE (Variational Auto Encoder) is responsible for compressing and encoding inputs into latent space, as well as decoding outputs back into image space.
-
-When you generate an image using text-to-image, multiple steps occur in latent space:
-1. Random noise is generated at the chosen height and width. The noise’s characteristics are dictated by the chosen (or not chosen) seed. This noise tensor is passed into latent space. We’ll call this noise A.
-1. Using a model’s U-Net, a noise predictor examines noise A, and the words tokenized by CLIP from your prompt (conditioning). It generates its own noise tensor to predict what the final image might look like in latent space. We’ll call this noise B.
-1. Noise B is subtracted from noise A in an attempt to create a final latent image indicative of the inputs. This step is repeated for the number of sampler steps chosen.
-1. The VAE decodes the final latent image from latent space into image space.
-
-image-to-image is a similar process, with only step 1 being different:
-1. The input image is decoded from image space into latent space by the VAE. Noise is then added to the input latent image. Denoising Strength dictates how much noise is added, 0 being none, and 1 being all-encompassing. We’ll call this noise A. The process is then the same as steps 2-4 in the text-to-image explanation above. 
-
-Furthermore, a model provides the CLIP prompt tokenizer, the VAE, and a U-Net (where noise prediction occurs given a prompt and initial noise tensor).
-
-A noise scheduler (eg. DPM++ 2M Karras) schedules the subtraction of noise from the latent image across the sampler steps chosen (step 3 above). Less noise is usually subtracted at higher sampler steps. 
-
-## Node Types (Base Nodes)
-
-| Node <img width=160 align="right"> | Function                                                                              |
-| ---------------------------------- | --------------------------------------------------------------------------------------|
-| Add                                | Adds two numbers |
-| CannyImageProcessor                | Canny edge detection for ControlNet |
-| ClipSkip                           | Skip layers in clip text_encoder model |
-| Collect                            | Collects values into a collection |
-| Prompt (Compel)                    | Parse prompt using compel package to conditioning |
-| ContentShuffleImageProcessor       | Applies content shuffle processing to image |
-| ControlNet                         | Collects ControlNet info to pass to other nodes |
-| CvInpaint                          | Simple inpaint using opencv |
-| Divide                             | Divides two numbers |
-| DynamicPrompt                      | Parses a prompt using adieyal/dynamic prompt's random or combinatorial generator |
-| FloatLinearRange                   | Creates a range |
-| HedImageProcessor                  | Applies HED edge detection to image |
-| ImageBlur                          | Blurs an image |
-| ImageChannel                       | Gets a channel from an image |
-| ImageCollection                    | Load a collection of images and provide it as output |
-| ImageConvert                       | Converts an image to a different mode |
-| ImageCrop                          | Crops an image to a specified box. The box can be outside of the image. |
-| ImageInverseLerp                   | Inverse linear interpolation of all pixels of an image |
-| ImageLerp                          | Linear interpolation of all pixels of an image |
-| ImageMultiply                      | Multiplies two images together using `PIL.ImageChops.Multiply()` |
-| ImagePaste                         | Pastes an image into another image |
-| ImageProcessor                     | Base class for invocations that reprocess images for ControlNet |
-| ImageResize                        | Resizes an image to specific dimensions |
-| ImageScale                         | Scales an image by a factor |
-| ImageToLatents                     | Scales latents by a given factor |
-| InfillColor                        | Infills transparent areas of an image with a solid color |
-| InfillPatchMatch                   | Infills transparent areas of an image using the PatchMatch algorithm |
-| InfillTile                         | Infills transparent areas of an image with tiles of the image |
-| Inpaint                            | Generates an image using inpaint |
-| Iterate                            | Iterates over a list of items |
-| LatentsToImage                     | Generates an image from latents |
-| LatentsToLatents                   | Generates latents using latents as base image |
-| LeresImageProcessor                | Applies leres processing to image |
-| LineartAnimeImageProcessor         | Applies line art anime processing to image |
-| LineartImageProcessor              | Applies line art processing to image |
-| LoadImage                          | Load an image and provide it as output |
-| Lora Loader                        | Apply selected lora to unet and text_encoder |
-| Model Loader                       | Loads a main model, outputting its submodels |
-| MaskFromAlpha                      | Extracts the alpha channel of an image as a mask |
-| MediapipeFaceProcessor             | Applies mediapipe face processing to image |
-| MidasDepthImageProcessor           | Applies Midas depth processing to image |
-| MlsdImageProcessor                 | Applied MLSD processing to image |
-| Multiply                           | Multiplies two numbers |
-| Noise                              | Generates latent noise |
-| NormalbaeImageProcessor            | Applies NormalBAE processing to image |
-| OpenposeImageProcessor             | Applies Openpose processing to image |
-| ParamFloat                         | A float parameter |
-| ParamInt                           | An integer parameter |
-| PidiImageProcessor                 | Applies PIDI processing to an image |
-| Progress Image                     | Displays the progress image in the Node Editor |
-| RandomInit                         | Outputs a single random integer |
-| RandomRange                        | Creates a collection of random numbers |
-| Range                              | Creates a range of numbers from start to stop with step |
-| RangeOfSize                        | Creates a range from start to start + size with step |
-| ResizeLatents                      | Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8. |
-| RestoreFace                        | Restores faces in the image |
-| ScaleLatents                       | Scales latents by a given factor |
-| SegmentAnythingProcessor           | Applies segment anything processing to image |
-| ShowImage                          | Displays a provided image, and passes it forward in the pipeline |
-| StepParamEasing                    | Experimental per-step parameter for easing for denoising steps |
-| Subtract                           | Subtracts two numbers |
-| TextToLatents                      | Generates latents from conditionings |
-| TileResampleProcessor              | Bass class for invocations that preprocess images for ControlNet |
-| Upscale                            | Upscales an image |
-| VAE Loader                         | Loads a VAE model, outputting a VaeLoaderOutput |
-| ZoeDepthImageProcessor             | Applies Zoe depth processing to image |
-
-## Node Grouping Concepts
-
-There are several node grouping concepts that can be examined with a narrow focus. These (and other) groupings can be pieced together to make up functional graph setups, and are important to understanding how groups of nodes work together as part of a whole. Note that the screenshots below aren't examples of complete functioning node graphs (see Examples).
-
-### Noise
-
-As described, an initial noise tensor is necessary for the latent diffusion process. As a result, all non-image *ToLatents nodes require a noise node input.  
-
-<img width="654" alt="groupsnoise" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/2e8d297e-ad55-4d27-bc93-c119dad2a2c5">
-
-### Conditioning
-
-As described, conditioning is necessary for the latent diffusion process, whether empty or not. As a result, all non-image *ToLatents nodes require positive and negative conditioning inputs. Conditioning is reliant on a CLIP tokenizer provided by the Model Loader node.
-
-<img width="1024" alt="groupsconditioning" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/f8f7ad8a-8d9c-418e-b5ad-1437b774b27e">
-
-### Image Space & VAE
-
-The ImageToLatents node doesn't require a noise node input, but requires a VAE input to convert the image from image space into latent space. In reverse, the LatentsToImage node requires a VAE input to convert from latent space back into image space.
-
-<img width="637" alt="groupsimgvae" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/dd99969c-e0a8-4f78-9b17-3ffe179cef9a">
-
-### Defined & Random Seeds
-
-It is common to want to use both the same seed (for continuity) and random seeds (for variance). To define a seed, simply enter it into the 'Seed' field on a noise node. Conversely, the RandomInt node generates a random integer between 'Low' and 'High', and can be used as input to the 'Seed' edge point on a noise node to randomize your seed.
-
-<img width="922" alt="groupsrandseed" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/af55bc20-60f6-438e-aba5-3ec871443710">
-
-### Control
-
-Control means to guide the diffusion process to adhere to a defined input or structure. Control can be provided as input to non-image *ToLatents nodes from ControlNet nodes. ControlNet nodes usually require an image processor which converts an input image for use with ControlNet.
-
-<img width="805" alt="groupscontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/cc9c5de7-23a7-46c8-bbad-1f3609d999a6">
-
-### LoRA
-
-The Lora Loader node lets you load a LoRA (say that ten times fast) and pass it as output to both the Prompt (Compel) and non-image *ToLatents nodes. A model's CLIP tokenizer is passed through the LoRA into Prompt (Compel), where it affects conditioning. A model's U-Net is also passed through the LoRA into a non-image *ToLatents node, where it affects noise prediction.
-
-<img width="993" alt="groupslora" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/630962b0-d914-4505-b3ea-ccae9b0269da">
-
-### Scaling
-
-Use the ImageScale, ScaleLatents, and Upscale nodes to upscale images and/or latent images. The chosen method differs across contexts. However, be aware that latents are already noisy and compressed at their original resolution; scaling an image could produce more detailed results.
-
-<img width="644" alt="groupsallscale" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/99314f05-dd9f-4b6d-b378-31de55346a13">
-
-### Iteration + Multiple Images as Input
-
-Iteration is a common concept in any processing, and means to repeat a process with given input. In nodes, you're able to use the Iterate node to iterate through collections usually gathered by the Collect node. The Iterate node has many potential uses, from processing a collection of images one after another, to varying seeds across multiple image generations and more. This screenshot demonstrates how to collect several images and pass them out one at a time.
-
-<img width="788" alt="groupsiterate" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/4af5ca27-82c9-4018-8c5b-024d3ee0a121">
-
-### Multiple Image Generation + Random Seeds
-
-Multiple image generation in the node editor is done using the RandomRange node. In this case, the 'Size' field represents the number of images to generate. As RandomRange produces a collection of integers, we need to add the Iterate node to iterate through the collection. 
-
-To control seeds across generations takes some care. The first row in the screenshot will generate multiple images with different seeds, but using the same RandomRange parameters across invocations will result in the same group of random seeds being used across the images, producing repeatable results. In the second row, adding the RandomInt node as input to RandomRange's 'Seed' edge point will ensure that seeds are varied across all images across invocations, producing varied results.
-
-<img width="1027" alt="groupsmultigenseeding" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/518d1b2b-fed1-416b-a052-ab06552521b3">
-
-## Examples
-
-With our knowledge of node grouping and the diffusion process, let’s break down some basic graphs in the nodes editor. Note that a node's options can be overridden by inputs from other nodes. These examples aren't strict rules to follow and only demonstrate some basic configurations.
-
-### Basic text-to-image Node Graph
-
-<img width="875" alt="nodest2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/17c67720-c376-4db8-94f0-5e00381a61ee">
-
-- Model Loader: A necessity to generating images (as we’ve read above). We choose our model from the dropdown. It outputs a U-Net, CLIP tokenizer, and VAE.
-- Prompt (Compel): Another necessity. Two prompt nodes are created. One will output positive conditioning (what you want, ‘dog’), one will output negative (what you don’t want, ‘cat’). They both input the CLIP tokenizer that the Model Loader node outputs.
-- Noise: Consider this noise A from step one of the text-to-image explanation above. Choose a seed number, width, and height.
-- TextToLatents: This node takes many inputs for converting and processing text & noise from image space into latent space, hence the name TextTo**Latents**. In this setup, it inputs positive and negative conditioning from the prompt nodes for processing (step 2 above). It inputs noise from the noise node for processing (steps 2 & 3 above). Lastly, it inputs a U-Net from the Model Loader node for processing (step 2 above). It outputs latents for use in the next LatentsToImage node. Choose number of sampler steps, CFG scale, and scheduler.
-- LatentsToImage: This node takes in processed latents from the TextToLatents node, and the model’s VAE from the Model Loader node which is responsible for decoding latents back into the image space, hence the name LatentsTo**Image**. This node is the last stop, and once the image is decoded, it is saved to the gallery.
-
-### Basic image-to-image Node Graph
-
-<img width="998" alt="nodesi2i" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/3f2c95d5-cee7-4415-9b79-b46ee60a92fe">
-
-- Model Loader: Choose a model from the dropdown.
-- Prompt (Compel): Two prompt nodes. One positive (dog), one negative (dog). Same CLIP inputs from the Model Loader node as before.
-- ImageToLatents: Upload a source image directly in the node window, via drag'n'drop from the gallery, or passed in as input. The ImageToLatents node inputs the VAE from the Model Loader node to decode the chosen image from image space into latent space, hence the name ImageTo**Latents**. It outputs latents for use in the next LatentsToLatents node. It also outputs the source image's width and height for use in the next Noise node if the final image is to be the same dimensions as the source image.
-- Noise: A noise tensor is created with the width and height of the source image, and connected to the next LatentsToLatents node. Notice the width and height fields are overridden by the input from the ImageToLatents width and height outputs.
-- LatentsToLatents: The inputs and options are nearly identical to TextToLatents, except that LatentsToLatents also takes latents as an input. Considering our source image is already converted to latents in the last ImageToLatents node, and text + noise are no longer the only inputs to process, we use the LatentsToLatents node. 
-- LatentsToImage: Like previously, the LatentsToImage node will use the VAE from the Model Loader as input to decode the latents from LatentsToLatents into image space, and save it to the gallery.
-
-### Basic ControlNet Node Graph
-
-<img width="703" alt="nodescontrol" src="https://github.com/ymgenesis/InvokeAI/assets/25252829/b02ded86-ceb4-44a2-9910-e19ad184d471">
-
-- Model Loader
-- Prompt (Compel)
-- Noise: Width and height of the CannyImageProcessor ControlNet image is passed in to set the dimensions of the noise passed to TextToLatents.
-- CannyImageProcessor: The CannyImageProcessor node is used to process the source image being used as a ControlNet. Each ControlNet processor node applies control in different ways, and has some different options to configure. Width and height are passed to noise, as mentioned. The processed ControlNet image is output to the ControlNet node.
-- ControlNet: Select the type of control model. In this case, canny is chosen as the CannyImageProcessor was used to generate the ControlNet image. Configure the control node options, and pass the control output to TextToLatents.
-- TextToLatents: Similar to the basic text-to-image example, except ControlNet is passed to the control input edge point.
-- LatentsToImage

docs/features/NSFW.md

@@ -31,22 +31,10 @@ turned on and off on the command line using `--nsfw_checker` and
 
 At installation time, InvokeAI will ask whether the checker should be
 activated by default (neither argument given on the command line). The
-response is stored in the InvokeAI initialization file
-(`invokeai.yaml` in the InvokeAI root directory).  You can change the
-default at any time by opening this file in a text editor and
-changing the line `nsfw_checker:` from true to false or vice-versa:
-
-
-```
-...
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    nsfw_checker: true
-    patchmatch: true
-    restore: true
-```
+response is stored in the InvokeAI initialization file (usually
+`.invokeai` in your home directory). You can change the default at any
+time by opening this file in a text editor and commenting or
+uncommenting the line `--nsfw_checker`.
 
 ## Caveats
 
@@ -91,3 +79,11 @@ generates. However, it does write metadata into the PNG data area,
 including the prompt used to generate the image and relevant parameter
 settings. These fields can be examined using the `sd-metadata.py`
 script that comes with the InvokeAI package.
+
+Note that several other Stable Diffusion distributions offer
+wavelet-based "invisible" watermarking. We have experimented with the
+library used to generate these watermarks and have reached the
+conclusion that while the watermarking library may be adding
+watermarks to PNG images, the currently available version is unable to
+retrieve them successfully. If and when a functioning version of the
+library becomes available, we will offer this feature as well.

docs/features/OTHER.md

@@ -18,16 +18,43 @@ Output Example:
 
 ## **Seamless Tiling**
 
-The seamless tiling mode causes generated images to seamlessly tile
-with itself creating repetitive wallpaper-like patterns. To use it,
-activate the Seamless Tiling option in the Web GUI and then select
-whether to tile on the X (horizontal) and/or Y (vertical) axes. Tiling
-will then be active for the next set of generations.
-
-A nice prompt to test seamless tiling with is:
+The seamless tiling mode causes generated images to seamlessly tile with itself. To use it, add the
+`--seamless` option when starting the script which will result in all generated images to tile, or
+for each `invoke>` prompt as shown here:
 
+```python
+invoke> "pond garden with lotus by claude monet" --seamless -s100 -n4
 ```
-pond garden with lotus by claude monet"
+
+By default this will tile on both the X and Y axes. However, you can also specify specific axes to tile on with `--seamless_axes`.
+Possible values are `x`, `y`, and `x,y`:
+```python
+invoke> "pond garden with lotus by claude monet" --seamless --seamless_axes=x -s100 -n4
+```
+
+---
+
+## **Shortcuts: Reusing Seeds**
+
+Since it is so common to reuse seeds while refining a prompt, there is now a shortcut as of version
+1.11. Provide a `-S` (or `--seed`) switch of `-1` to use the seed of the most recent image
+generated. If you produced multiple images with the `-n` switch, then you can go back further
+using `-2`, `-3`, etc. up to the first image generated by the previous command. Sorry, but you can't go
+back further than one command.
+
+Here's an example of using this to do a quick refinement. It also illustrates using the new `-G`
+switch to turn on upscaling and face enhancement (see previous section):
+
+```bash
+invoke> a cute child playing hopscotch -G0.5
+[...]
+outputs/img-samples/000039.3498014304.png: "a cute child playing hopscotch" -s50 -W512 -H512 -C7.5 -mk_lms -S3498014304
+
+# I wonder what it will look like if I bump up the steps and set facial enhancement to full strength?
+invoke> a cute child playing hopscotch -G1.0 -s100 -S -1
+reusing previous seed 3498014304
+[...]
+outputs/img-samples/000040.3498014304.png: "a cute child playing hopscotch" -G1.0 -s100 -W512 -H512 -C7.5 -mk_lms -S3498014304
 ```
 
 ---
@@ -46,27 +73,66 @@ This will tell the sampler to invest 25% of its effort on the tabby cat aspect o
 on the white duck aspect (surprisingly, this example actually works). The prompt weights can use any
 combination of integers and floating point numbers, and they do not need to add up to 1.
 
+---
+
+## **Filename Format**
+
+The argument `--fnformat` allows to specify the filename of the
+ image. Supported wildcards are all arguments what can be set such as
+ `perlin`, `seed`, `threshold`, `height`, `width`, `gfpgan_strength`,
+ `sampler_name`, `steps`, `model`, `upscale`, `prompt`, `cfg_scale`,
+ `prefix`.
+
+The following prompt
+```bash
+dream> a red car --steps 25 -C 9.8 --perlin 0.1 --fnformat {prompt}_steps.{steps}_cfg.{cfg_scale}_perlin.{perlin}.png
+```
+
+generates a file with the name: `outputs/img-samples/a red car_steps.25_cfg.9.8_perlin.0.1.png`
+
+---
+
 ## **Thresholding and Perlin Noise Initialization Options**
 
-Under the Noise section of the Web UI, you will find two options named
-Perlin Noise and Noise Threshold. [Perlin
-noise](https://en.wikipedia.org/wiki/Perlin_noise) is a type of
-structured noise used to simulate terrain and other natural
-textures. The slider controls the percentage of perlin noise that will
-be mixed into the image at the beginning of generation. Adding a little
-perlin noise to a generation will alter the image substantially.
-
-The noise threshold limits the range of the latent values during
-sampling and helps combat the oversharpening seem with higher CFG
-scale values.
+Two new options are the thresholding (`--threshold`) and the perlin noise initialization (`--perlin`) options. Thresholding limits the range of the latent values during optimization, which helps combat oversaturation with higher CFG scale values. Perlin noise initialization starts with a percentage (a value ranging from 0 to 1) of perlin noise mixed into the initial noise. Both features allow for more variations and options in the course of generating images.
 
 For better intuition into what these options do in practice:
 
 ![here is a graphic demonstrating them both](../assets/truncation_comparison.jpg)
 
-In generating this graphic, perlin noise at initialization was
-programmatically varied going across on the diagram by values 0.0,
-0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied
-going down from 0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are
-fixed using the prompt "a portrait of a beautiful young lady" a CFG of
-20, 100 steps, and a seed of 1950357039.
+In generating this graphic, perlin noise at initialization was programmatically varied going across on the diagram by values 0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 0.9, 1.0; and the threshold was varied going down from
+0, 1, 2, 3, 4, 5, 10, 20, 100. The other options are fixed, so the initial prompt is as follows (no thresholding or perlin noise):
+
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 0 --perlin 0
+```
+
+Here's an example of another prompt used when setting the threshold to 5 and perlin noise to 0.2:
+
+```bash
+invoke> "a portrait of a beautiful young lady" -S 1950357039 -s 100 -C 20 -A k_euler_a --threshold 5 --perlin 0.2
+```
+
+!!! note
+
+    currently the thresholding feature is only implemented for the k-diffusion style samplers, and empirically appears to work best with `k_euler_a` and `k_dpm_2_a`. Using 0 disables thresholding. Using 0 for perlin noise disables using perlin noise for initialization. Finally, using 1 for perlin noise uses only perlin noise for initialization.
+
+---
+
+## **Simplified API**
+
+For programmers who wish to incorporate stable-diffusion into other products, this repository
+includes a simplified API for text to image generation, which lets you create images from a prompt
+in just three lines of code:
+
+```bash
+from ldm.generate import Generate
+g       = Generate()
+outputs = g.txt2img("a unicorn in manhattan")
+```
+
+Outputs is a list of lists in the format [filename1,seed1],[filename2,seed2]...].
+
+Please see the documentation in ldm/generate.py for more information.
+
+---

docs/features/POSTPROCESS.md

@@ -8,6 +8,12 @@ title: Postprocessing
 
 This extension provides the ability to restore faces and upscale images.
 
+Face restoration and upscaling can be applied at the time you generate the
+images, or at any later time against a previously-generated PNG file, using the
+[!fix](#fixing-previously-generated-images) command.
+[Outpainting and outcropping](OUTPAINTING.md) can only be applied after the
+fact.
+
 ## Face Fixing
 
 The default face restoration module is GFPGAN. The default upscale is
@@ -17,7 +23,8 @@ Real-ESRGAN. For an alternative face restoration module, see
 As of version 1.14, environment.yaml will install the Real-ESRGAN package into
 the standard install location for python packages, and will put GFPGAN into a
 subdirectory of "src" in the InvokeAI directory. Upscaling with Real-ESRGAN
-should "just work" without further intervention. Simply indicate the desired scale on
+should "just work" without further intervention. Simply pass the `--upscale`
+(`-U`) option on the `invoke>` command line, or indicate the desired scale on
 the popup in the Web GUI.
 
 **GFPGAN** requires a series of downloadable model files to work. These are
@@ -34,75 +41,48 @@ reconstruction.
 
 ### Upscaling
 
-Open the upscaling dialog by clicking on the "expand" icon located
-above the image display area in the Web UI:
+`-U : <upscaling_factor> <upscaling_strength>`
 
-<figure markdown>
-![upscale1](../assets/features/upscale-dialog.png)
-</figure>
+The upscaling prompt argument takes two values. The first value is a scaling
+factor and should be set to either `2` or `4` only. This will either scale the
+image 2x or 4x respectively using different models.
 
-There are three different upscaling parameters that you can
-adjust. The first is the scale itself, either 2x or 4x.
+You can set the scaling stength between `0` and `1.0` to control intensity of
+the of the scaling. This is handy because AI upscalers generally tend to smooth
+out texture details. If you wish to retain some of those for natural looking
+results, we recommend using values between `0.5 to 0.8`.
 
-The second is the "Denoising Strength." Higher values will smooth out
-the image and remove digital chatter, but may lose fine detail at
-higher values.
-
-Third, "Upscale Strength" allows you to adjust how the You can set the
-scaling stength between `0` and `1.0` to control the intensity of the
-scaling. AI upscalers generally tend to smooth out texture details. If
-you wish to retain some of those for natural looking results, we
-recommend using values between `0.5 to 0.8`.
-
-[This figure](../assets/features/upscaling-montage.png) illustrates
-the effects of denoising and strength. The original image was 512x512,
-4x scaled to 2048x2048. The "original" version on the upper left was
-scaled using simple pixel averaging. The remainder use the ESRGAN
-upscaling algorithm at different levels of denoising and strength.
-
-<figure markdown>
-![upscaling](../assets/features/upscaling-montage.png){ width=720 }
-</figure>
-
-Both denoising and strength default to 0.75.
+If you do not explicitly specify an upscaling_strength, it will default to 0.75.
 
 ### Face Restoration
 
-InvokeAI offers alternative two face restoration algorithms,
-[GFPGAN](https://github.com/TencentARC/GFPGAN) and
-[CodeFormer](https://huggingface.co/spaces/sczhou/CodeFormer). These
-algorithms improve the appearance of faces, particularly eyes and
-mouths. Issues with faces are less common with the latest set of
-Stable Diffusion models than with the original 1.4 release, but the
-restoration algorithms can still make a noticeable improvement in
-certain cases. You can also apply restoration to old photographs you
-upload.
+`-G : <facetool_strength>`
 
-To access face restoration, click the "smiley face" icon in the
-toolbar above the InvokeAI image panel. You will be presented with a
-dialog that offers a choice between the two algorithm and sliders that
-allow you to adjust their parameters. Alternatively, you may open the
-left-hand accordion panel labeled "Face Restoration" and have the
-restoration algorithm of your choice applied to generated images
-automatically.
+This prompt argument controls the strength of the face restoration that is being
+applied. Similar to upscaling, values between `0.5 to 0.8` are recommended.
 
+You can use either one or both without any conflicts. In cases where you use
+both, the image will be first upscaled and then the face restoration process
+will be executed to ensure you get the highest quality facial features.
 
-Like upscaling, there are a number of parameters that adjust the face
-restoration output. GFPGAN has a single parameter, `strength`, which
-controls how much the algorithm is allowed to adjust the
-image. CodeFormer has two parameters, `strength`, and `fidelity`,
-which together control the quality of the output image as described in
-the [CodeFormer project
-page](https://shangchenzhou.com/projects/CodeFormer/). Default values
-are 0.75 for both parameters, which achieves a reasonable balance
-between changing the image too much and not enough.
+`--save_orig`
 
-[This figure](../assets/features/restoration-montage.png) illustrates
-the effects of adjusting GFPGAN and CodeFormer parameters.
+When you use either `-U` or `-G`, the final result you get is upscaled or face
+modified. If you want to save the original Stable Diffusion generation, you can
+use the `-save_orig` prompt argument to save the original unaffected version
+too.
 
-<figure markdown>
-![upscaling](../assets/features/restoration-montage.png){ width=720 }
-</figure>
+### Example Usage
+
+```bash
+invoke> "superman dancing with a panda bear" -U 2 0.6 -G 0.4
+```
+
+This also works with img2img:
+
+```bash
+invoke> "a man wearing a pineapple hat" -I path/to/your/file.png -U 2 0.5 -G 0.6
+```
 
 !!! note
 
@@ -115,8 +95,69 @@ the effects of adjusting GFPGAN and CodeFormer parameters.
     process is complete. While the image generation is taking place, you will still be able to preview
     the base images.
 
+If you wish to stop during the image generation but want to upscale or face
+restore a particular generated image, pass it again with the same prompt and
+generated seed along with the `-U` and `-G` prompt arguments to perform those
+actions.
+
+## CodeFormer Support
+
+This repo also allows you to perform face restoration using
+[CodeFormer](https://github.com/sczhou/CodeFormer).
+
+In order to setup CodeFormer to work, you need to download the models like with
+GFPGAN. You can do this either by running `invokeai-configure` or by manually
+downloading the
+[model file](https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth)
+and saving it to `ldm/invoke/restoration/codeformer/weights` folder.
+
+You can use `-ft` prompt argument to swap between CodeFormer and the default
+GFPGAN. The above mentioned `-G` prompt argument will allow you to control the
+strength of the restoration effect.
+
+### CodeFormer Usage
+
+The following command will perform face restoration with CodeFormer instead of
+the default gfpgan.
+
+`<prompt> -G 0.8 -ft codeformer`
+
+### Other Options
+
+- `-cf` - cf or CodeFormer Fidelity takes values between `0` and `1`. 0 produces
+  high quality results but low accuracy and 1 produces lower quality results but
+  higher accuacy to your original face.
+
+The following command will perform face restoration with CodeFormer. CodeFormer
+will output a result that is closely matching to the input face.
+
+`<prompt> -G 1.0 -ft codeformer -cf 0.9`
+
+The following command will perform face restoration with CodeFormer. CodeFormer
+will output a result that is the best restoration possible. This may deviate
+slightly from the original face. This is an excellent option to use in
+situations when there is very little facial data to work with.
+
+`<prompt> -G 1.0 -ft codeformer -cf 0.1`
+
+## Fixing Previously-Generated Images
+
+It is easy to apply face restoration and/or upscaling to any
+previously-generated file. Just use the syntax
+`!fix path/to/file.png <options>`. For example, to apply GFPGAN at strength 0.8
+and upscale 2X for a file named `./outputs/img-samples/000044.2945021133.png`,
+just run:
+
+```bash
+invoke> !fix ./outputs/img-samples/000044.2945021133.png -G 0.8 -U 2
+```
+
+A new file named `000044.2945021133.fixed.png` will be created in the output
+directory. Note that the `!fix` command does not replace the original file,
+unlike the behavior at generate time.
+
 ## How to disable
 
 If, for some reason, you do not wish to load the GFPGAN and/or ESRGAN libraries,
 you can disable them on the invoke.py command line with the `--no_restore` and
-`--no_esrgan` options, respectively.
+`--no_upscale` options, respectively.

docs/features/PROMPTS.md

@@ -4,12 +4,77 @@ title: Prompting-Features
 
 # :octicons-command-palette-24: Prompting-Features
 
+## **Reading Prompts from a File**
+
+You can automate `invoke.py` by providing a text file with the prompts you want
+to run, one line per prompt. The text file must be composed with a text editor
+(e.g. Notepad) and not a word processor. Each line should look like what you
+would type at the invoke> prompt:
+
+```bash
+"a beautiful sunny day in the park, children playing" -n4 -C10
+"stormy weather on a mountain top, goats grazing" -s100
+"innovative packaging for a squid's dinner" -S137038382
+```
+
+Then pass this file's name to `invoke.py` when you invoke it:
+
+```bash
+python scripts/invoke.py --from_file "/path/to/prompts.txt"
+```
+
+You may also read a series of prompts from standard input by providing
+a filename of `-`. For example, here is a python script that creates a
+matrix of prompts, each one varying slightly:
+
+```bash
+#!/usr/bin/env python
+
+adjectives = ['sunny','rainy','overcast']
+samplers = ['k_lms','k_euler_a','k_heun']
+cfg = [7.5, 9, 11]
+
+for adj in adjectives:
+    for samp in samplers:
+        for cg in cfg:
+            print(f'a {adj} day -A{samp} -C{cg}')
+```
+
+It's output looks like this (abbreviated):
+
+```bash
+a sunny day -Aklms -C7.5
+a sunny day -Aklms -C9
+a sunny day -Aklms -C11
+a sunny day -Ak_euler_a -C7.5
+a sunny day -Ak_euler_a -C9
+...
+a overcast day -Ak_heun -C9
+a overcast day -Ak_heun -C11
+```
+
+To feed it to invoke.py, pass the filename of "-"
+
+```bash
+python matrix.py | python scripts/invoke.py --from_file -
+```
+
+When the script is finished, each of the 27 combinations
+of adjective, sampler and CFG will be executed.
+
+The command-line interface provides `!fetch` and `!replay` commands
+which allow you to read the prompts from a single previously-generated
+image or a whole directory of them, write the prompts to a file, and
+then replay them. Or you can create your own file of prompts and feed
+them to the command-line client from within an interactive session.
+See [Command-Line Interface](CLI.md) for details.
+
+---
+
 ## **Negative and Unconditioned Prompts**
 
-Any words between a pair of square brackets will instruct Stable
-Diffusion to attempt to ban the concept from the generated image. The
-same effect is achieved by placing words in the "Negative Prompts"
-textbox in the Web UI.
+Any words between a pair of square brackets will instruct Stable Diffusion to
+attempt to ban the concept from the generated image.
 
 ```text
 this is a test prompt [not really] to make you understand [cool] how this works.
@@ -22,9 +87,7 @@ Here's a prompt that depicts what it does.
 
 original prompt:
 
-`#!bash "A fantastical translucent pony made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve"`
-
-`#!bash parameters: steps=20, dimensions=512x768, CFG=7.5, Scheduler=k_euler_a, seed=1654590180`
+`#!bash "A fantastical translucent pony made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
 <figure markdown>
 
@@ -36,8 +99,7 @@ That image has a woman, so if we want the horse without a rider, we can
 influence the image not to have a woman by putting [woman] in the prompt, like
 this:
 
-`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]"`
-(same parameters as above)
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
 <figure markdown>
 
@@ -48,8 +110,7 @@ this:
 That's nice - but say we also don't want the image to be quite so blue. We can
 add "blue" to the list of negative prompts, so it's now [woman blue]:
 
-`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]"`
-(same parameters as above)
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
 <figure markdown>
 
@@ -60,8 +121,7 @@ add "blue" to the list of negative prompts, so it's now [woman blue]:
 Getting close - but there's no sense in having a saddle when our horse doesn't
 have a rider, so we'll add one more negative prompt: [woman blue saddle].
 
-`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]"`
-(same parameters as above)
+`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
 
 <figure markdown>
 
@@ -201,6 +261,19 @@ Prompt2prompt `.swap()` is not compatible with xformers, which will be temporari
 The `prompt2prompt` code is based off
 [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
 
+Note that `prompt2prompt` is not currently working with the runwayML inpainting
+model, and may never work due to the way this model is set up. If you attempt to
+use `prompt2prompt` you will get the original image back. However, since this
+model is so good at inpainting, a good substitute is to use the `clipseg` text
+masking option:
+
+```bash
+invoke> a fluffy cat eating a hotdot
+Outputs:
+[1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
+invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat
+```
+
 ### Escaping parantheses () and speech marks ""
 
 If the model you are using has parentheses () or speech marks "" as part of its
@@ -301,48 +374,6 @@ summoning up the concept of some sort of scifi creature? Let's find out.
 Indeed, removing the word "hybrid" produces an image that is more like what we'd
 expect.
 
-## Dynamic Prompts
-
-Dynamic Prompts are a powerful feature designed to produce a variety of prompts based on user-defined options. Using a special syntax, you can construct a prompt with multiple possibilities, and the system will automatically generate a series of permutations based on your settings. This is extremely beneficial for ideation, exploring various scenarios, or testing different concepts swiftly and efficiently.
-
-### Structure of a Dynamic Prompt
-
-A Dynamic Prompt comprises of regular text, supplemented with alternatives enclosed within curly braces {} and separated by a vertical bar |. For example: {option1|option2|option3}. The system will then select one of the options to include in the final prompt. This flexible system allows for options to be placed throughout the text as needed.
-
-Furthermore, Dynamic Prompts can designate multiple selections from a single group of options. This feature is triggered by prefixing the options with a numerical value followed by $$. For example, in {2$$option1|option2|option3}, the system will select two distinct options from the set.
-### Creating Dynamic Prompts
-
-To create a Dynamic Prompt, follow these steps:
-
-    Draft your sentence or phrase, identifying words or phrases with multiple possible options.
-    Encapsulate the different options within curly braces {}.
-    Within the braces, separate each option using a vertical bar |.
-    If you want to include multiple options from a single group, prefix with the desired number and $$.
-
-For instance: A {house|apartment|lodge|cottage} in {summer|winter|autumn|spring} designed in {2$$style1|style2|style3}.
-### How Dynamic Prompts Work
-
-Once a Dynamic Prompt is configured, the system generates an array of combinations using the options provided. Each group of options in curly braces is treated independently, with the system selecting one option from each group. For a prefixed set (e.g., 2$$), the system will select two distinct options.
-
-For example, the following prompts could be generated from the above Dynamic Prompt:
-
-    A house in summer designed in style1, style2
-    A lodge in autumn designed in style3, style1
-    A cottage in winter designed in style2, style3
-    And many more!
-
-When the `Combinatorial` setting is on, Invoke will disable the "Images" selection, and generate every combination up until the setting for Max Prompts is reached.
-When the `Combinatorial` setting is off, Invoke will randomly generate combinations up until the setting for Images has been reached.
-
-
-
-### Tips and Tricks for Using Dynamic Prompts
-
-Below are some useful strategies for creating Dynamic Prompts:
-
-    Utilize Dynamic Prompts to generate a wide spectrum of prompts, perfect for brainstorming and exploring diverse ideas.
-    Ensure that the options within a group are contextually relevant to the part of the sentence where they are used. For instance, group building types together, and seasons together.
-    Apply the 2$$ prefix when you want to incorporate more than one option from a single group. This becomes quite handy when mixing and matching different elements.
-    Experiment with different quantities for the prefix. For example, 3$$ will select three distinct options.
-    Be aware of coherence in your prompts. Although the system can generate all possible combinations, not all may semantically make sense. Therefore, carefully choose the options for each group.
-    Always review and fine-tune the generated prompts as needed. While Dynamic Prompts can help you generate a multitude of combinations, the final polishing and refining remain in your hands.
+In conclusion, prompt blending is great for exploring creative space, but can be
+difficult to direct. A forthcoming release of InvokeAI will feature more
+deterministic prompt weighting.

docs/features/TEXTUAL_INVERSION.md

@@ -1,10 +1,9 @@
 ---
-title: Training
+title: Textual-Inversion
 ---
 
-# :material-file-document: Training
+# :material-file-document: Textual Inversion
 
-# Textual Inversion Training
 ## **Personalizing Text-to-Image Generation**
 
 You may personalize the generated images to provide your own styles or objects
@@ -18,7 +17,7 @@ notebooks.
 
 You will need a GPU to perform training in a reasonable length of
 time, and at least 12 GB of VRAM. We recommend using the [`xformers`
-library](../installation/070_INSTALL_XFORMERS.md) to accelerate the
+library](../installation/070_INSTALL_XFORMERS) to accelerate the
 training process further. During training, about ~8 GB is temporarily
 needed in order to store intermediate models, checkpoints and logs.
 
@@ -47,23 +46,16 @@ start the front end by selecting choice (3):
 
 ```sh
 Do you want to generate images using the
-1: Browser-based UI
-2: Command-line interface
-3: Run textual inversion training
-4: Merge models (diffusers type only)
-5: Download and install models
-6: Change InvokeAI startup options
-7: Re-run the configure script to fix a broken install
-8: Open the developer console
-9: Update InvokeAI
-10: Command-line help
-Q: Quit
-
-Please enter 1-10, Q: [1] 
+1. command-line
+2. browser-based UI
+3. textual inversion training
+4. open the developer console
+Please enter 1, 2, 3, or 4: [1] 3
 ```
 
 From the command line, with the InvokeAI virtual environment active,
-you can launch the front end with the command `invokeai-ti --gui`.
+you can launch the front end with the command `textual_inversion
+--gui`.
 
 This will launch a text-based front end that will look like this:
 
@@ -235,12 +227,12 @@ It accepts a large number of arguments, which can be summarized by
 passing the `--help` argument:
 
 ```sh
-invokeai-ti --help
+textual_inversion --help
 ```
 
 Typical usage is shown here:
 ```sh
-invokeai-ti \
+textual_inversion \
        --model=stable-diffusion-1.5 \
        --resolution=512 \
        --learnable_property=style \
@@ -259,14 +251,6 @@ invokeai-ti \
        --only_save_embeds
 ```
 
-## Troubleshooting
-
-### `Cannot load embedding for <trigger>. It was trained on a model with token dimension 1024, but the current model has token dimension 768`
-
-Messages like this indicate you trained the embedding on a different base model than the currently selected one.
-
-For example, in the error above, the training was done on SD2.1 (768x768) but it was used on SD1.5 (512x512).
-
 ## Reading
 
 For more information on textual inversion, please see the following
@@ -283,4 +267,4 @@ resources:
 
 ---
 
-copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team
+copyright (c) 2023, Lincoln Stein and the InvokeAI Development Team

docs/features/VARIATIONS.md

@@ -6,7 +6,9 @@ title: Variations
 
 ## Intro
 
-InvokeAI's support for variations enables you to do the following:
+Release 1.13 of SD-Dream adds support for image variations.
+
+You are able to do the following:
 
 1. Generate a series of systematic variations of an image, given a prompt. The
    amount of variation from one image to the next can be controlled.
@@ -28,7 +30,19 @@ The prompt we will use throughout is:
 This will be indicated as `#!bash "prompt"` in the examples below.
 
 First we let SD create a series of images in the usual way, in this case
-requesting six iterations.
+requesting six iterations:
+
+```bash
+invoke> lucy lawless as xena, warrior princess, character portrait, high resolution -n6
+...
+Outputs:
+./outputs/Xena/000001.1579445059.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1579445059
+./outputs/Xena/000001.1880768722.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S1880768722
+./outputs/Xena/000001.332057179.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S332057179
+./outputs/Xena/000001.2224800325.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S2224800325
+./outputs/Xena/000001.465250761.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S465250761
+./outputs/Xena/000001.3357757885.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -S3357757885
+```
 
 <figure markdown>
 ![var1](../assets/variation_walkthru/000001.3357757885.png)
@@ -39,16 +53,22 @@ requesting six iterations.
 
 ## Step 2 - Generating Variations
 
-Let's try to generate some variations on this image. We select the "*"
-symbol in the line of icons above the image in order to fix the prompt
-and seed. Then we open up the "Variations" section of the generation
-panel and use the slider to set the variation amount to 0.2. The
-higher this value, the more each generated image will differ from the
-previous one.
+Let's try to generate some variations. Using the same seed, we pass the argument
+`-v0.1` (or --variant_amount), which generates a series of variations each
+differing by a variation amount of 0.2. This number ranges from `0` to `1.0`,
+with higher numbers being larger amounts of variation.
 
-Now we run the prompt a second time, requesting six iterations. You
-will see six images that are thematically related to each other. Try
-increasing and decreasing the variation amount and see what happens.
+```bash
+invoke> "prompt" -n6 -S3357757885 -v0.2
+...
+Outputs:
+./outputs/Xena/000002.784039624.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 784039624:0.2 -S3357757885
+./outputs/Xena/000002.3647897225.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 3647897225:0.2 -S3357757885
+./outputs/Xena/000002.917731034.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 917731034:0.2 -S3357757885
+./outputs/Xena/000002.4116285959.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 4116285959:0.2 -S3357757885
+./outputs/Xena/000002.1614299449.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1614299449:0.2 -S3357757885
+./outputs/Xena/000002.1335553075.png: "prompt" -s50 -W512 -H512 -C7.5 -Ak_lms -V 1335553075:0.2 -S3357757885
+```
 
 ### **Variation Sub Seeding**
 

docs/features/WEB.md

@@ -5,14 +5,11 @@ title: InvokeAI Web Server
 # :material-web: InvokeAI Web Server
 
 As of version 2.0.0, this distribution comes with a full-featured web server
-(see screenshot).
-
-To use it, launch the `invoke.sh`/`invoke.bat` script and select
-option (2). Alternatively, with the InvokeAI environment active, run
-the `invokeai` script by adding the `--web` option:
+(see screenshot). To use it, run the `invoke.py` script by adding the `--web`
+option:
 
 ```bash
-invokeai --web
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web
 ```
 
 You can then connect to the server by pointing your web browser at
@@ -22,23 +19,17 @@ address of the host you are running it on, or the wildcard `0.0.0.0`. For
 example:
 
 ```bash
-invoke.sh --host 0.0.0.0
+(invokeai) ~/InvokeAI$ python3 scripts/invoke.py --web --host 0.0.0.0
 ```
 
-or
+## Quick guided walkthrough of the WebGUI's features
 
-```bash
-invokeai --web --host 0.0.0.0
-```
-
-## Quick guided walkthrough of the WebUI's features
-
-While most of the WebUI's features are intuitive, here is a guided walkthrough
+While most of the WebGUI's features are intuitive, here is a guided walkthrough
 through its various components.
 
 ![Invoke Web Server - Major Components](../assets/invoke-web-server-1.png){:width="640px"}
 
-The screenshot above shows the Text to Image tab of the WebUI. There are three
+The screenshot above shows the Text to Image tab of the WebGUI. There are three
 main sections:
 
 1. A **control panel** on the left, which contains various settings for text to
@@ -72,14 +63,12 @@ From top to bottom, these are:
 1. Text to Image - generate images from text
 2. Image to Image - from an uploaded starting image (drawing or photograph)
    generate a new one, modified by the text prompt
-3. Unified Canvas - Interactively combine multiple images, extend them
-   with outpainting,and modify interior portions of the image with
-   inpainting, erase portions of a starting image and have the AI fill in
-   the erased region from a text prompt.
-4. Node Editor - this panel allows you to create
-   pipelines of common operations and combine them into workflows.
-5. Model Manager - this panel allows you to import and configure new
-   models using URLs, local paths, or HuggingFace diffusers repo_ids.
+3. Inpainting (pending) - Interactively erase portions of a starting image and
+   have the AI fill in the erased region from a text prompt.
+4. Outpainting (pending) - Interactively add blank space to the borders of a
+   starting image and fill in the background from a text prompt.
+5. Postprocessing (pending) - Interactively postprocess generated images using a
+   variety of filters.
 
 The inpainting, outpainting and postprocessing tabs are currently in
 development. However, limited versions of their features can already be accessed
@@ -87,18 +76,18 @@ through the Text to Image and Image to Image tabs.
 
 ## Walkthrough
 
-The following walkthrough will exercise most (but not all) of the WebUI's
+The following walkthrough will exercise most (but not all) of the WebGUI's
 feature set.
 
 ### Text to Image
 
-1. Launch the WebUI using `python scripts/invoke.py --web` and connect to it
+1. Launch the WebGUI using `python scripts/invoke.py --web` and connect to it
    with your browser by accessing `http://localhost:9090`. If the browser and
    server are running on different machines on your LAN, add the option
    `--host 0.0.0.0` to the launch command line and connect to the machine
    hosting the web server using its IP address or domain name.
 
-2. If all goes well, the WebUI should come up and you'll see a green
+2. If all goes well, the WebGUI should come up and you'll see a green
    `connected` message on the upper right.
 
 #### Basics
@@ -245,7 +234,7 @@ walkthrough.
 
 2.  Drag-and-drop the Lincoln-and-Parrot image into the Image panel, or click
     the blank area to get an upload dialog. The image will load into an area
-    marked _Initial Image_. (The WebUI will also load the most
+    marked _Initial Image_. (The WebGUI will also load the most
     recently-generated image from the gallery into a section on the left, but
     this image will be replaced in the next step.)
 
@@ -295,9 +284,13 @@ initial image" icons are located.
 
 ![Invoke Web Server - Use as Image Links](../assets/invoke-web-server-9.png){:width="640px"}
 
-### Unified Canvas
+## Parting remarks
 
-See the [Unified Canvas Guide](UNIFIED_CANVAS.md)
+This concludes the walkthrough, but there are several more features that you can
+explore. Please check out the [Command Line Interface](CLI.md) documentation for
+further explanation of the advanced features that were not covered here.
+
+The WebGUI is only rapid development. Check back regularly for updates!
 
 ## Reference
 
@@ -341,9 +334,11 @@ the settings configured in the toolbar.
 
 See below for additional documentation related to each feature:
 
+- [Core Prompt Settings](./CLI.md)
 - [Variations](./VARIATIONS.md)
 - [Upscaling](./POSTPROCESS.md#upscaling)
 - [Image to Image](./IMG2IMG.md)
+- [Inpainting](./INPAINTING.md)
 - [Other](./OTHER.md)
 
 #### Invocation Gallery

docs/features/index.md

@@ -2,53 +2,4 @@
 title: Overview
 ---
 
-Here you can find the documentation for InvokeAI's various features.
-
-## The Basics
-### * The [Web User Interface](WEB.md)
-Guide to the Web interface. Also see the [WebUI Hotkeys Reference Guide](WEBUIHOTKEYS.md)
-
-### * The [Unified Canvas](UNIFIED_CANVAS.md)
-Build complex scenes by combine and modifying multiple images in a stepwise
-fashion. This feature combines img2img, inpainting and outpainting in
-a single convenient digital artist-optimized user interface.
-
-## Image Generation
-### * [Prompt Engineering](PROMPTS.md)
-Get the images you want with the InvokeAI  prompt engineering language.
-
-## * The [Concepts Library](CONCEPTS.md)
-Add custom subjects and styles using HuggingFace's repository of embeddings.
-
-### * [Image-to-Image Guide](IMG2IMG.md)
-Use a seed image to build new creations in the CLI.
-
-### * [Generating Variations](VARIATIONS.md)
-Have an image you like and want to generate many more like it? Variations
-are the ticket.
-
-## Model Management
-
-## * [Model Installation](../installation/050_INSTALLING_MODELS.md)
-Learn how to import third-party models and switch among them. This
-guide also covers optimizing models to load quickly.
-
-## * [Merging Models](MODEL_MERGING.md)
-Teach an old model new tricks. Merge 2-3 models together to create a
-new model that combines characteristics of the originals.
-
-## * [Textual Inversion](TRAINING.md)
-Personalize models by adding your own style or subjects.
-
-# Other Features
-
-## * [The NSFW Checker](NSFW.md)
-Prevent InvokeAI from displaying unwanted racy images.
-
-## * [Controlling Logging](LOGGING.md)
-Control how InvokeAI logs status messages.
-
-## * [Miscellaneous](OTHER.md)
-Run InvokeAI on Google Colab, generate images with repeating patterns,
-batch process a file of prompts, increase the "creativity" of image
-generation by adding initial noise, and more!
+Here you can find the documentation for different features.

docs/index.html

@@ -0,0 +1,19 @@
+<!-- HTML for static distribution bundle build -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8">
+    <title>Swagger UI</title>
+    <link rel="stylesheet" type="text/css" href="swagger-ui/swagger-ui.css" />
+    <link rel="stylesheet" type="text/css" href="swagger-ui/index.css" />
+    <link rel="icon" type="image/png" href="swagger-ui/favicon-32x32.png" sizes="32x32" />
+    <link rel="icon" type="image/png" href="swagger-ui/favicon-16x16.png" sizes="16x16" />
+  </head>
+
+  <body>
+    <div id="swagger-ui"></div>
+    <script src="swagger-ui/swagger-ui-bundle.js" charset="UTF-8"> </script>
+    <script src="swagger-ui/swagger-ui-standalone-preset.js" charset="UTF-8"> </script>
+    <script src="swagger-ui/swagger-initializer.js" charset="UTF-8"> </script>
+  </body>
+</html>

docs/index.md

@@ -13,7 +13,6 @@ title: Home
 
 <div align="center" markdown>
 
-
 [![project logo](assets/invoke_ai_banner.png)](https://github.com/invoke-ai/InvokeAI)
 
 [![discord badge]][discord link]
@@ -68,7 +67,7 @@ title: Home
 implementation of Stable Diffusion, the open source text-to-image and
 image-to-image generator. It provides a streamlined process with various new
 features and options to aid the image generation process. It runs on Windows,
-Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
+Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM.
 
 **Quick links**: [<a href="https://discord.gg/ZmtBAhwWhy">Discord Server</a>]
 [<a href="https://github.com/invoke-ai/InvokeAI/">Code and Downloads</a>] [<a
@@ -82,6 +81,28 @@ Q&A</a>]
 
     This fork is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates. They will help aid diagnose issues faster.
 
+## :octicons-package-dependencies-24: Installation
+
+This fork is supported across Linux, Windows and Macintosh. Linux users can use
+either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
+driver).
+
+First time users, please see
+[Automated Installer](installation/INSTALL_AUTOMATED.md) for a walkthrough of
+getting InvokeAI up and running on your system. For alternative installation and
+upgrade instructions, please see:
+[InvokeAI Installation Overview](installation/)
+
+Users who wish to make use of the **PyPatchMatch** inpainting functions
+will need to perform a bit of extra work to enable this
+module. Instructions can be found at [Installing
+PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md).
+
+If you have an NVIDIA card, you can benefit from the significant
+memory savings and performance benefits provided by Facebook Lab's
+**xFormers** module. Instructions for Linux and Windows users can be found
+at [Installing xFormers](installation/070_INSTALL_XFORMERS.md).
+
 ## :fontawesome-solid-computer: Hardware Requirements
 
 ### :octicons-cpu-24: System
@@ -101,121 +122,141 @@ images in full-precision mode:
 - GTX 1650 series cards
 - GTX 1660 series cards
 
-### :fontawesome-solid-memory: Memory and Disk
+### :fontawesome-solid-memory: Memory
 
 - At least 12 GB Main Memory RAM.
+
+### :fontawesome-regular-hard-drive: Disk
+
 - At least 18 GB of free disk space for the machine learning model, Python, and
   all its dependencies.
 
-## :octicons-package-dependencies-24: Installation
+!!! info
 
-This fork is supported across Linux, Windows and Macintosh. Linux users can use
-either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm
-driver).
+    Precision is auto configured based on the device. If however you encounter errors like
+    `expected type Float but found Half` or `not implemented for Half` you can try starting
+    `invoke.py` with the `--precision=float32` flag:
 
-### [Installation Getting Started Guide](installation)
-#### [Automated Installer](installation/010_INSTALL_AUTOMATED.md)
-This method is recommended for 1st time users
-#### [Manual Installation](installation/020_INSTALL_MANUAL.md)
-This method is recommended for experienced users and developers
-#### [Docker Installation](installation/040_INSTALL_DOCKER.md)
-This method is recommended for those familiar with running Docker containers
-### Other Installation Guides
-  - [PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md)
-  - [XFormers](installation/070_INSTALL_XFORMERS.md)
-  - [CUDA and ROCm Drivers](installation/030_INSTALL_CUDA_AND_ROCM.md)
-  - [Installing New Models](installation/050_INSTALLING_MODELS.md)
+    ```bash
+    (invokeai) ~/InvokeAI$ python scripts/invoke.py --full_precision
+    ```
 
 ## :octicons-gift-24: InvokeAI Features
 
-### The InvokeAI Web Interface
-- [WebUI overview](features/WEB.md)
-- [WebUI hotkey reference guide](features/WEBUIHOTKEYS.md)
-- [WebUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
-
-<!-- separator -->
-
-### Image Management
-- [Image2Image](features/IMG2IMG.md)
-- [Adding custom styles and subjects](features/CONCEPTS.md)
-- [Upscaling and Face Reconstruction](features/POSTPROCESS.md)
-- [Other Features](features/OTHER.md)
-
-<!-- separator -->
-### Model Management
-- [Installing](installation/050_INSTALLING_MODELS.md)
-- [Model Merging](features/MODEL_MERGING.md)
-- [Style/Subject Concepts and Embeddings](features/CONCEPTS.md)
-- [Not Safe for Work (NSFW) Checker](features/NSFW.md)
+- [The InvokeAI Web Interface](features/WEB.md) -
+[WebGUI hotkey reference guide](features/WEBUIHOTKEYS.md) -
+[WebGUI Unified Canvas for Img2Img, inpainting and outpainting](features/UNIFIED_CANVAS.md)
+<!-- seperator -->
+- [The Command Line Interace](features/CLI.md) -
+[Image2Image](features/IMG2IMG.md) - [Inpainting](features/INPAINTING.md) -
+[Outpainting](features/OUTPAINTING.md) -
+[Adding custom styles and subjects](features/CONCEPTS.md) -
+[Upscaling and Face Reconstruction](features/POSTPROCESS.md)
 <!-- seperator -->
-### Prompt Engineering
-- [Prompt Syntax](features/PROMPTS.md)
 - [Generating Variations](features/VARIATIONS.md)
+<!-- seperator -->
+- [Prompt Engineering](features/PROMPTS.md)
+<!-- seperator -->
+- [Model Merging](features/MODEL_MERGING.md)
+<!-- seperator -->
+- Miscellaneous
+  - [NSFW Checker](features/NSFW.md)
+  - [Embiggen upscaling](features/EMBIGGEN.md)
+  - [Other](features/OTHER.md)
 
-### InvokeAI Configuration
-- [Guide to InvokeAI Runtime Settings](features/CONFIGURATION.md)
+## :octicons-log-16: Latest Changes
 
-## :octicons-log-16: Important Changes Since Version 2.3
+### v2.2.4 <small>(11 December 2022)</small>
 
-### Nodes
+#### the `invokeai` directory
 
-Behind the scenes, InvokeAI has been completely rewritten to support
-"nodes," small unitary operations that can be combined into graphs to
-form arbitrary workflows. For example, there is a prompt node that
-processes the prompt string and feeds it to a text2latent node that
-generates a latent image. The latents are then fed to a latent2image
-node that translates the latent image into a PNG.
+Previously there were two directories to worry about, the directory that
+contained the InvokeAI source code and the launcher scripts, and the `invokeai`
+directory that contained the models files, embeddings, configuration and
+outputs. With the 2.2.4 release, this dual system is done away with, and
+everything, including the `invoke.bat` and `invoke.sh` launcher scripts, now
+live in a directory named `invokeai`. By default this directory is located in
+your home directory (e.g. `\Users\yourname` on Windows), but you can select
+where it goes at install time.
 
-The WebGUI has a node editor that allows you to graphically design and
-execute custom node graphs. The ability to save and load graphs is
-still a work in progress, but coming soon.
+After installation, you can delete the install directory (the one that the zip
+file creates when it unpacks). Do **not** delete or move the `invokeai`
+directory!
 
-### Command-Line Interface Retired
+##### Initialization file `invokeai/invokeai.init`
 
-The original "invokeai" command-line interface has been retired. The
-`invokeai` command will now launch a new command-line client that can
-be used by developers to create and test nodes. It is not intended to
-be used for routine image generation or manipulation.
+You can place frequently-used startup options in this file, such as the default
+number of steps or your preferred sampler. To keep everything in one place, this
+file has now been moved into the `invokeai` directory and is named
+`invokeai.init`.
 
-To launch the Web GUI from the command-line, use the command
-`invokeai-web` rather than the traditional `invokeai --web`.
+#### To update from Version 2.2.3
 
-### ControlNet
+The easiest route is to download and unpack one of the 2.2.4 installer files.
+When it asks you for the location of the `invokeai` runtime directory, respond
+with the path to the directory that contains your 2.2.3 `invokeai`. That is, if
+`invokeai` lives at `C:\Users\fred\invokeai`, then answer with `C:\Users\fred`
+and answer "Y" when asked if you want to reuse the directory.
 
-This version of InvokeAI features ControlNet, a system that allows you
-to achieve exact poses for human and animal figures by providing a
-model to follow. Full details are found in [ControlNet](features/CONTROLNET.md)
+The `update.sh` (`update.bat`) script that came with the 2.2.3 source installer
+does not know about the new directory layout and won't be fully functional.
 
-### New Schedulers
+#### To update to 2.2.5 (and beyond) there's now an update path.
 
-The list of schedulers has been completely revamped and brought up to date:
+As they become available, you can update to more recent versions of InvokeAI
+using an `update.sh` (`update.bat`) script located in the `invokeai` directory.
+Running it without any arguments will install the most recent version of
+InvokeAI. Alternatively, you can get set releases by running the `update.sh`
+script with an argument in the command shell. This syntax accepts the path to
+the desired release's zip file, which you can find by clicking on the green
+"Code" button on this repository's home page.
 
-| **Short Name** | **Scheduler**                   | **Notes**                   |
-|----------------|---------------------------------|-----------------------------|
-| **ddim**       | DDIMScheduler                   |                             |
-| **ddpm**       | DDPMScheduler                   |                             |
-| **deis**       | DEISMultistepScheduler          |                             |
-| **lms**        | LMSDiscreteScheduler            |                             |
-| **pndm**       | PNDMScheduler                   |                             |
-| **heun**       | HeunDiscreteScheduler           | original noise schedule     |
-| **heun_k**     | HeunDiscreteScheduler           | using karras noise schedule |
-| **euler**      | EulerDiscreteScheduler          | original noise schedule     |
-| **euler_k**    | EulerDiscreteScheduler          | using karras noise schedule |
-| **kdpm_2**     | KDPM2DiscreteScheduler          |                             |
-| **kdpm_2_a**   | KDPM2AncestralDiscreteScheduler |                             |
-| **dpmpp_2s**   | DPMSolverSinglestepScheduler    |                             |
-| **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
-| **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
-| **unipc**      | UniPCMultistepScheduler         | CPU only                    |
+#### Other 2.2.4 Improvements
 
-Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
+- Fix InvokeAI GUI initialization by @addianto in #1687
+- fix link in documentation by @lstein in #1728
+- Fix broken link by @ShawnZhong in #1736
+- Remove reference to binary installer by @lstein in #1731
+- documentation fixes for 2.2.3 by @lstein in #1740
+- Modify installer links to point closer to the source installer by @ebr in
+  #1745
+- add documentation warning about 1650/60 cards by @lstein in #1753
+- Fix Linux source URL in installation docs by @andybearman in #1756
+- Make install instructions discoverable in readme by @damian0815 in #1752
+- typo fix by @ofirkris in #1755
+- Non-interactive model download (support HUGGINGFACE_TOKEN) by @ebr in #1578
+- fix(srcinstall): shell installer - cp scripts instead of linking by @tildebyte
+  in #1765
+- stability and usage improvements to binary & source installers by @lstein in
+  #1760
+- fix off-by-one bug in cross-attention-control by @damian0815 in #1774
+- Eventually update APP_VERSION to 2.2.3 by @spezialspezial in #1768
+- invoke script cds to its location before running by @lstein in #1805
+- Make PaperCut and VoxelArt models load again by @lstein in #1730
+- Fix --embedding_directory / --embedding_path not working by @blessedcoolant in
+  #1817
+- Clean up readme by @hipsterusername in #1820
+- Optimized Docker build with support for external working directory by @ebr in
+  #1544
+- disable pushing the cloud container by @mauwii in #1831
+- Fix docker push github action and expand with additional metadata by @ebr in
+  #1837
+- Fix Broken Link To Notebook by @VedantMadane in #1821
+- Account for flat models by @spezialspezial in #1766
+- Update invoke.bat.in isolate environment variables by @lynnewu in #1833
+- Arch Linux Specific PatchMatch Instructions & fixing conda install on linux by
+  @SammCheese in #1848
+- Make force free GPU memory work in img2img by @addianto in #1844
+- New installer by @lstein
+
+For older changelogs, please visit the
+**[CHANGELOG](CHANGELOG/#v223-2-december-2022)**.
 
 ## :material-target: Troubleshooting
 
-Please check out our **[:material-frequently-asked-questions:
-Troubleshooting
-Guide](installation/010_INSTALL_AUTOMATED.md#troubleshooting)** to
-get solutions for common installation problems and other issues.
+Please check out our
+**[:material-frequently-asked-questions: Q&A](help/TROUBLESHOOT.md)** to get
+solutions for common installation problems and other issues.
 
 ## :octicons-repo-push-24: Contributing
 
@@ -241,6 +282,11 @@ thank them for their time, hard work and effort.
 For support, please use this repository's GitHub Issues tracking service. Feel
 free to send me an email if you use and like the script.
 
-Original portions of the software are Copyright (c) 2022-23
-by [The InvokeAI Team](https://github.com/invoke-ai).
+Original portions of the software are Copyright (c) 2020
+[Lincoln D. Stein](https://github.com/lstein)
 
+## :octicons-book-24: Further Reading
+
+Please see the original README for more information on this software and
+underlying algorithm, located in the file
+[README-CompViz.md](other/README-CompViz.md).

docs/installation/010_INSTALL_AUTOMATED.md

@@ -6,106 +6,57 @@ title: Installing with the Automated Installer
 
 ## Introduction
 
-The automated installer is a Python script that automates the steps
-needed to install and run InvokeAI on a stock computer running recent
-versions of Linux, MacOS or Windows. It will leave you with a version
-that runs a stable version of InvokeAI with the option to upgrade to
-experimental versions later.
+The automated installer is a shell script that attempts to automate every step
+needed to install and run InvokeAI on a stock computer running recent versions
+of Linux, MacOS or Windows. It will leave you with a version that runs a stable
+version of InvokeAI with the option to upgrade to experimental versions later.
 
 ## Walk through
 
-1.  <a name="hardware_requirements">**Hardware Requirements**: </a>Make sure that your system meets the [hardware
-    requirements](../index.md#hardware-requirements) and has the
-    appropriate GPU drivers installed. For a system with an NVIDIA
-    card installed, you will need to install the CUDA driver, while
-    AMD-based cards require the ROCm driver. In most cases, if you've
-    already used the system for gaming or other graphics-intensive
-    tasks, the appropriate drivers will already be installed. If
-    unsure, check the [GPU Driver Guide](030_INSTALL_CUDA_AND_ROCM.md)
+1.  Make sure that your system meets the
+    [hardware requirements](../index.md#hardware-requirements) and has the
+    appropriate GPU drivers installed. In particular, if you are a Linux user
+    with an AMD GPU installed, you may need to install the
+    [ROCm driver](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
 
     !!! info "Required Space"
 
-        Installation requires roughly 18G of free disk space to load
-        the libraries and recommended model weights files.
+        Installation requires roughly 18G of free disk space to load the libraries and
+        recommended model weights files.
 
-        Regardless of your destination disk, your *system drive*
-        (`C:\` on Windows, `/` on macOS/Linux) requires at least 6GB
-        of free disk space to download and cache python
-        dependencies.
+        Regardless of your destination disk, your *system drive* (`C:\` on Windows, `/` on macOS/Linux) requires at least 6GB of free disk space to download and cache python dependencies. NOTE for Linux users: if your temporary directory is mounted as a `tmpfs`, ensure it has sufficient space.
 
-	NOTE for Linux users: if your temporary directory is mounted
-        as a `tmpfs`, ensure it has sufficient space.
+2.  Check that your system has an up-to-date Python installed. To do this, open
+    up a command-line window ("Terminal" on Linux and Macintosh, "Command" or
+    "Powershell" on Windows) and type `python --version`. If Python is
+    installed, it will print out the version number. If it is version `3.9.1` or `3.10.x`, you meet requirements.
 
-2.  <a name="software_requirements">**Software Requirements**: </a>Check that your system has an up-to-date Python installed. To do
-    this, open up a command-line window ("Terminal" on Linux and
-    Macintosh, "Command" or "Powershell" on Windows) and type `python
-    --version`. If Python is installed, it will print out the version
-    number. If it is version `3.9.*` or `3.10.*`, you meet
-    requirements. We do not recommend using Python 3.11 or higher,
-    as not all the libraries that InvokeAI depends on work properly
-    with this version.
+    !!! warning "At this time we do not recommend Python 3.11"
 
-    !!! warning "What to do if you have an unsupported version"
+    !!! warning "If you see an older version, or get a command not found error"
 
-        Go to [Python Downloads](https://www.python.org/downloads/)
-        and download the appropriate installer package for your
-        platform. We recommend [Version
-        3.10.9](https://www.python.org/downloads/release/python-3109/),
+        Go to [Python Downloads](https://www.python.org/downloads/) and
+        download the appropriate installer package for your platform. We recommend
+        [Version 3.10.9](https://www.python.org/downloads/release/python-3109/),
         which has been extensively tested with InvokeAI.
 
+
     _Please select your platform in the section below for platform-specific
     setup requirements._
 
-    === "Windows"
-        During the Python configuration process, look out for a
-        checkbox to add Python to your PATH and select it. If the
-        install script complains that it can't find python, then open
-        the Python installer again and choose "Modify" existing
-        installation.
+    === "Windows users"
 
-        Installation requires an up to date version of the Microsoft
-        Visual C libraries. Please install the 2015-2022 libraries
-        available here:
-        https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
+        - During the Python configuration process,
+        look out for a checkbox to add Python to your PATH
+        and select it. If the install script complains that it can't
+        find python, then open the Python installer again and choose
+        "Modify" existing installation.
 
-        Please double-click on the file `WinLongPathsEnabled.reg` and
-        accept the dialog box that asks you if you wish to modify your registry.
-        This activates long filename support on your system and will prevent
-        mysterious errors during installation.
+        - Installation requires an up to date version of the Microsoft Visual C libraries. Please install the 2015-2022 libraries available here: https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170
 
-    === "Linux"
-         To install an appropriate version of Python on Ubuntu 22.04
-         and higher, run the following:
+    === "Mac users"
 
-         ```
-         sudo apt update
-         sudo apt install -y python3 python3-pip python3-venv
-         sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
-         ```
-
-         On Ubuntu 20.04, the process is slightly different:
-
-         ```
-         sudo apt update
-         sudo apt install -y software-properties-common
-         sudo add-apt-repository -y ppa:deadsnakes/ppa
-         sudo apt install -y python3.10 python3-pip python3.10-venv
-         sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
-         ```
-
-         Both `python` and `python3` commands are now pointing at
-         Python3.10. You can still access older versions of Python by
-         calling `python2`, `python3.8`, etc.
-
-         Linux systems require a couple of additional graphics
-         libraries to be installed for proper functioning of
-         `python3-opencv`. Please run the following:
-
-         `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
-
-    === "Mac"
-
-        After installing Python, you may need to run the
+        - After installing Python, you may need to run the
         following command from the Terminal in order to install the Web
         certificates needed to download model data from https sites. If
         you see lots of CERTIFICATE ERRORS during the last part of the
@@ -113,81 +64,109 @@ experimental versions later.
 
             `/Applications/Python\ 3.10/Install\ Certificates.command`
 
-        You may need to install the Xcode command line tools. These
+        -  You may need to install the Xcode command line tools. These
         are a set of tools that are needed to run certain applications in a
-        Terminal, including InvokeAI. This package is provided
-        directly by Apple. To install, open a terminal window and run `xcode-select --install`. You will get a macOS system popup guiding you through the
-        install. If you already have them installed, you will instead see some
-        output in the Terminal advising you that the tools are already installed. More information can be found at [FreeCode Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
+        Terminal, including InvokeAI. This package is provided directly by Apple.
 
-3.  **Download the Installer**: The InvokeAI installer is distributed as a ZIP files. Go to the
-    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest),
-    and look for a file named:
+              - To install, open a terminal window and run `xcode-select
+              --install`. You will get a macOS system popup guiding you through the
+              install. If you already have them installed, you will instead see some
+              output in the Terminal advising you that the tools are already installed.
 
-    - InvokeAI-installer-v2.X.X.zip
+              - More information can be found here:
+                https://www.freecodecamp.org/news/install-xcode-command-line-tools/
 
-    where "2.X.X" is the latest released version. The file is located
-    at the very bottom of the release page, under **Assets**.
+    === "Linux users"
 
-4.  **Unpack the installer**: Unpack the zip file into a convenient directory. This will create a new
-    directory named "InvokeAI-Installer". When unpacked, the directory
-    will look like this:
+        For reasons that are not entirely clear, installing the correct version of Python can be a bit of a challenge on Ubuntu, Linux Mint, Pop!_OS, and other Debian-derived distributions.
 
-    <figure markdown>
-    ![zipfile-screenshot](../assets/installer-walkthrough/unpacked-zipfile.png)
-    </figure>
+        On Ubuntu 22.04 and higher, run the following:
 
-5.  **Launch the installer script from the desktop**: If you are using a desktop GUI, double-click the installer file
-    appropriate for your platform. It will be named `install.bat` on
-    Windows systems and `install.sh` on Linux and Macintosh
-    systems. Be aware that your system's file browser may suppress the
-    display of the file extension.
+        ```
+        sudo apt update
+        sudo apt install -y python3 python3-pip python3-venv
+        sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
+        ```
 
-    On Windows systems if you get an "Untrusted Publisher" warning.
-    Click on "More Info" and then select "Run Anyway." You trust us, right?
+        On Ubuntu 20.04, the process is slightly different:
 
-6.  **[Alternative] Launch the installer script from the command line**: Alternatively, from the command line, run the shell script or .bat file:
+        ```
+        sudo apt update
+        sudo apt install -y software-properties-common
+        sudo add-apt-repository -y ppa:deadsnakes/ppa
+        sudo apt install python3.10 python3-pip python3.10-venv
+        sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
+        ```
+
+        Both `python` and `python3` commands are now pointing at Python3.10. You can still access older versions of Python by calling `python2`, `python3.8`, etc.
+
+        Linux systems require a couple of additional graphics libraries to be installed for proper functioning of `python3-opencv`. Please run the following:
+
+        `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx`
+
+3.  The source installer is distributed in ZIP files. Go to the
+    [latest release](https://github.com/invoke-ai/InvokeAI/releases/latest), and
+    look for a series of files named:
+
+    - InvokeAI-installer-2.X.X.zip
+
+    (Where 2.X.X is the current release number).
+
+    Download the latest release.
+
+4.  Unpack the zip file into a convenient directory. This will create a new
+    directory named "InvokeAI-Installer". This example shows how this would look
+    using the `unzip` command-line tool, but you may use any graphical or
+    command-line Zip extractor:
+
+    ```cmd
+    C:\Documents\Linco> unzip InvokeAI-installer-2.X.X-windows.zip
+    Archive:  C: \Linco\Downloads\InvokeAI-installer-2.X.X-windows.zip
+    creating: InvokeAI-Installer\
+    inflating: InvokeAI-Installer\install.bat
+    inflating: InvokeAI-Installer\readme.txt
+    ...
+    ```
+
+    After successful installation, you can delete the `InvokeAI-Installer`
+    directory.
+
+5.  **Windows only** Please double-click on the file WinLongPathsEnabled.reg and
+    accept the dialog box that asks you if you wish to modify your registry.
+    This activates long filename support on your system and will prevent
+    mysterious errors during installation.
+
+6.  If you are using a desktop GUI, double-click the installer file. It will be
+    named `install.bat` on Windows systems and `install.sh` on Linux and
+    Macintosh systems.
+
+    On Windows systems you will probably get an "Untrusted Publisher" warning.
+    Click on "More Info" and select "Run Anyway." You trust us, right?
+
+7.  Alternatively, from the command line, run the shell script or .bat file:
 
     ```cmd
     C:\Documents\Linco> cd InvokeAI-Installer
-    C:\Documents\Linco\invokeAI> .\install.bat
+    C:\Documents\Linco\invokeAI> install.bat
     ```
 
-7.  **Select the location to install InvokeAI**: The script will ask you to choose where to install InvokeAI. Select a
+8.  The script will ask you to choose where to install InvokeAI. Select a
     directory with at least 18G of free space for a full install. InvokeAI and
     all its support files will be installed into a new directory named
     `invokeai` located at the location you specify.
 
-    <figure markdown>
-    ![confirm-install-directory-screenshot](../assets/installer-walkthrough/confirm-directory.png)
-    </figure>
-
     - The default is to install the `invokeai` directory in your home directory,
       usually `C:\Users\YourName\invokeai` on Windows systems,
       `/home/YourName/invokeai` on Linux systems, and `/Users/YourName/invokeai`
       on Macintoshes, where "YourName" is your login name.
 
-    -If you have previously installed InvokeAI, you will be asked to
-     confirm whether you want to reinstall into this directory.  You
-     may choose to reinstall, in which case your version will be upgraded,
-     or choose a different directory.
-
     - The script uses tab autocompletion to suggest directory path completions.
       Type part of the path (e.g. "C:\Users") and press ++tab++ repeatedly
       to suggest completions.
 
-8.  **Select your GPU**: The installer will autodetect your platform and will request you to
-    confirm the type of GPU your graphics card has. On Linux systems,
-    you will have the choice of CUDA (NVidia cards), ROCm (AMD cards),
-    or CPU (no graphics acceleration). On Windows, you'll have the
-    choice of CUDA vs CPU, and on Macs you'll be offered CPU only. When
-    you select CPU on M1 or M2 Macintoshes, you will get MPS-based
-    graphics acceleration without installing additional drivers. If you
-    are unsure what GPU you are using, you can ask the installer to
-    guess.
-
-9.  **Watch it go!**: Sit back and let the install script work. It will install the third-party
-    libraries needed by InvokeAI and the application itself.
+9.  Sit back and let the install script work. It will install the third-party
+    libraries needed by InvokeAI, then download the current InvokeAI release and
+    install it.
 
     Be aware that some of the library download and install steps take a long
     time. In particular, the `pytorch` package is quite large and often appears
@@ -197,141 +176,25 @@ experimental versions later.
     minutes and nothing is happening, you can interrupt the script with ^C. You
     may restart it and it will pick up where it left off.
 
-    <figure markdown>
-    ![initial-settings-screenshot](../assets/installer-walkthrough/settings-form.png)
-    </figure>
+10. After installation completes, the installer will launch the configuration script, which will guide you through the first-time process
+    of selecting one or more Stable Diffusion model weights files, downloading
+    and configuring them. We provide a list of popular models that InvokeAI
+    performs well with. However, you can add more weight files later on using
+    the command-line client or the Web UI. See
+    [Installing Models](050_INSTALLING_MODELS.md) for details.
 
-10. **Post-install Configuration**: After installation completes, the
-    installer will launch the configuration form, which will guide you
-    through the first-time process of adjusting some of InvokeAI's
-    startup settings. To move around this form use ctrl-N for
-    &lt;N&gt;ext and ctrl-P for &lt;P&gt;revious, or use &lt;tab&gt;
-    and shift-&lt;tab&gt; to move forward and back. Once you are in a
-    multi-checkbox field use the up and down cursor keys to select the
-    item you want, and &lt;space&gt; to toggle it on and off.  Within
-    a directory field, pressing &lt;tab&gt; will provide autocomplete
-    options.
+    Note that the main Stable Diffusion weights file is protected by a license
+    agreement that you must agree to in order to use. The script will list the
+    steps you need to take to create an account on the official site that hosts
+    the weights files, accept the agreement, and provide an access token that
+    allows InvokeAI to legally download and install the weights files.
 
-    Generally the defaults are fine, and you can come back to this screen at
-    any time to tweak your system. Here are the options you can adjust:
+    If you have already downloaded the weights file(s) for another Stable
+    Diffusion distribution, you may skip this step (by selecting "skip" when
+    prompted) and configure InvokeAI to use the previously-downloaded files. The
+    process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
 
-    - ***Output directory for images***
-      This is the path to a directory in which InvokeAI will store all its
-      generated images.
-
-    - ***NSFW checker***
-      If checked, InvokeAI will test images for potential sexual content
-      and blur them out if found. Note that the NSFW checker consumes
-      an additional 0.6 GB of VRAM on top of the 2-3 GB of VRAM used
-      by most image models. If you have a low VRAM GPU (4-6 GB), you
-      can reduce out of memory errors by disabling the checker.
-
-    - ***HuggingFace Access Token***
-      InvokeAI has the ability to download embedded styles and subjects
-      from the HuggingFace Concept Library on-demand. However, some of
-      the concept library files are password protected. To make download
-      smoother, you can set up an account at huggingface.co, obtain an
-      access token, and paste it into this field. Note that you paste
-      to this screen using ctrl-shift-V
-
-    - ***Free GPU memory after each generation***
-        This is useful for low-memory machines and helps minimize the
-	amount of GPU VRAM used by InvokeAI.
-
-    - ***Enable xformers support if available***
-        If the xformers library was successfully installed, this will activate
-	it to reduce memory consumption and increase rendering speed noticeably.
-	Note that xformers has the side effect of generating slightly different
-	images even when presented with the same seed and other settings.
-
-    - ***Force CPU to be used on GPU systems***
-        This will use the (slow) CPU rather than the accelerated GPU. This
-	can be used to generate images on systems that don't have a compatible
-	GPU.
-
-    - ***Precision***
-        This controls whether to use float32 or float16 arithmetic.
-	float16 uses less memory but is also slightly less accurate.
-	Ordinarily the right arithmetic is picked automatically ("auto"),
-	but you may have to use float32 to get images on certain systems
-	and graphics cards. The "autocast" option is deprecated and
-	shouldn't be used unless you are asked to by a member of the team.
-
-    - ***Number of models to cache in CPU memory***
-        This allows you to keep models in memory and switch rapidly among
-	them rather than having them load from disk each time. This slider
-	controls how many models to keep loaded at once. Each
-	model will use 2-4 GB of RAM, so use this cautiously
-
-    - ***Directory containing embedding/textual inversion files***
-        This is the directory in which you can place custom embedding
-	files (.pt or .bin). During startup, this directory will be
-	scanned and InvokeAI will print out the text terms that
-	are available to trigger the embeddings.
-
-    At the bottom of the screen you will see a checkbox for accepting
-    the CreativeML Responsible AI License. You need to accept the license
-    in order to download Stable Diffusion models from the next screen.
-
-    _You can come back to the startup options form_ as many times as you like.
-    From the `invoke.sh` or `invoke.bat` launcher, select option (6) to relaunch
-    this script. On the command line, it is named `invokeai-configure`.
-
-11. **Downloading Models**: After you press `[NEXT]` on the screen, you will be taken
-    to another screen that prompts you to download a series of starter models. The ones
-    we recommend are preselected for you, but you are encouraged to use the checkboxes to
-    pick and choose.
-    You will probably wish to download `autoencoder-840000` for use with models that
-    were trained with an older version of the Stability VAE.
-
-    <figure markdown>
-    ![select-models-screenshot](../assets/installer-walkthrough/installing-models.png)
-    </figure>
-    
-    Below the preselected list of starter models is a large text field which you can use
-    to specify a series of models to import. You can specify models in a variety of formats,
-    each separated by a space or newline. The formats accepted are:
-
-    - The path to a .ckpt or .safetensors file. On most systems, you can drag a file from
-      the file browser to the textfield to automatically paste the path. Be sure to remove
-      extraneous quotation marks and other things that come along for the ride.
-
-    - The path to a directory containing a combination of `.ckpt` and `.safetensors` files.
-      The directory will be scanned from top to bottom (including subfolders) and any
-      file that can be imported will be.
-
-    - A URL pointing to a `.ckpt` or `.safetensors` file. You can cut
-      and paste directly from a web page, or simply drag the link from the web page
-      or navigation bar. (You can also use ctrl-shift-V to paste into this field)
-      The file will be downloaded and installed.
-      
-    - The HuggingFace repository ID (repo_id) for a `diffusers` model. These IDs have
-       the format _author_name/model_name_, as in `andite/anything-v4.0`
-      
-    - The path to a local directory containing a `diffusers`
-      model. These directories always have the file `model_index.json`
-      at their top level.
-
-    _Select a directory for models to import_ You may select a local
-    directory for autoimporting at startup time. If you select this
-    option, the directory you choose will be scanned for new
-    .ckpt/.safetensors files each time InvokeAI starts up, and any new
-    files will be automatically imported and made available for your
-    use.
-
-    _Convert imported models into diffusers_ When legacy checkpoint
-    files are imported, you may select to use them unmodified (the
-    default) or to convert them into `diffusers` models. The latter
-    load much faster and have slightly better rendering performance,
-    but not all checkpoint files can be converted. Note that Stable Diffusion
-    Version 2.X files are **only** supported in `diffusers` format and will
-    be converted regardless.
-     
-     _You can come back to the model install form_ as many times as you like.
-     From the `invoke.sh` or `invoke.bat` launcher, select option (5) to relaunch
-     this script. On the command line, it is named `invokeai-model-install`.
-
-12. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
+11. The script will now exit and you'll be ready to generate some images. Look
     for the directory `invokeai` installed in the location you chose at the
     beginning of the install session. Look for a shell script named `invoke.sh`
     (Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
@@ -342,83 +205,49 @@ experimental versions later.
     C:\Documents\Linco\invokeAI> invoke.bat
     ```
 
-    - The `invoke.bat` (`invoke.sh`) script will give you the choice
-      of starting (1) the command-line interface, (2) the web GUI, (3)
-      textual inversion training, and (4) model merging.
+    - The `invoke.bat` (`invoke.sh`) script will give you the choice of starting
+      (1) the command-line interface, or (2) the web GUI. If you start the
+      latter, you can load the user interface by pointing your browser at
+      http://localhost:9090.
 
-    - By default, the script will launch the web interface. When you
-      do this, you'll see a series of startup messages ending with
-      instructions to point your browser at
-      http://localhost:9090. Click on this link to open up a browser
-      and start exploring InvokeAI's features.
-
-12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
-    customize its behavior. For example, you can change the location of the
-    image output directory or balance memory usage vs performance. See
-    [Configuration](../features/CONFIGURATION.md) for a full list of the options.
-
-    - To set defaults that will take effect every time you launch InvokeAI,
-      use a text editor (e.g. Notepad) to exit the file
-      `invokeai\invokeai.init`. It contains a variety of examples that you can
-      follow to add and modify launch options.
-
-    - The launcher script also offers you an option labeled "open the developer
+    - The script also offers you a third option labeled "open the developer
       console". If you choose this option, you will be dropped into a
       command-line interface in which you can run python commands directly,
       access developer tools, and launch InvokeAI with customized options.
 
+12. You can launch InvokeAI with several different command-line arguments that
+    customize its behavior. For example, you can change the location of the
+    image output directory, or select your favorite sampler. See the
+    [Command-Line Interface](../features/CLI.md) for a full list of the options.
 
-    !!! warning "Do not move or remove the `invokeai` directory"
-      
-        The `invokeai` directory contains the `invokeai` application, its
-        configuration files, the model weight files, and outputs of image generation.
-        Once InvokeAI is installed, do not move or remove this directory."
+        - To set defaults that will take effect every time you launch InvokeAI,
+        use a text editor (e.g. Notepad) to exit the file
+        `invokeai\invokeai.init`. It contains a variety of examples that you can
+        follow to add and modify launch options.
 
+!!! warning "The `invokeai` directory contains the `invokeai` application, its
+configuration files, the model weight files, and outputs of image generation.
+Once InvokeAI is installed, do not move or remove this directory."
 
 ## Troubleshooting
 
 ### _Package dependency conflicts_
 
-If you have previously installed InvokeAI or another Stable Diffusion
-package, the installer may occasionally pick up outdated libraries and
-either the installer or `invoke` will fail with complaints about
-library conflicts. In this case, run the `invoke.sh`/`invoke.bat`
-command and enter the Developer's Console by picking option (5). This
-will take you to a command-line prompt.
+If you have previously installed InvokeAI or another Stable Diffusion package,
+the installer may occasionally pick up outdated libraries and either the
+installer or `invoke` will fail with complaints about library conflicts. You can
+address this by entering the `invokeai` directory and running `update.sh`, which
+will bring InvokeAI up to date with the latest libraries.
 
-Then give this command:
+### ldm from pypi
 
-`pip install InvokeAI --force-reinstall`
+!!! warning
 
-This should fix the issues.
-
-### InvokeAI runs extremely slowly on Linux or Windows systems
-
-The most frequent cause of this problem is when the installation
-process installed the CPU-only version of the torch machine-learning
-library, rather than a version that takes advantage of GPU
-acceleration. To confirm this issue, look at the InvokeAI startup
-messages. If you see a message saying ">> Using device CPU", then
-this is what happened.
-
-To fix this problem, first determine whether you have an NVidia or an
-AMD GPU. The former uses the CUDA driver, and the latter uses ROCm
-(only available on Linux). Then run the `invoke.sh`/`invoke.bat`
-command and enter the Developer's Console by picking option (5). This
-will take you to a command-line prompt.
-
-Then type the following commands:
-
-=== "NVIDIA System"
-    ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu117
-    pip install xformers
-    ```
-
-=== "AMD System"
-    ```bash
-    pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
-    ```
+    Some users have tried to correct dependency problems by installing
+    the `ldm` package from PyPi.org. Unfortunately this is an unrelated package that
+    has nothing to do with the 'latent diffusion model' used by InvokeAI. Installing
+    ldm will make matters worse. If you've installed ldm, uninstall it with
+    `pip uninstall ldm`.
 
 ### Corrupted configuration file
 
@@ -443,53 +272,7 @@ the [InvokeAI Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
 visit our [Discord Server](https://discord.gg/ZmtBAhwWhy) for interactive
 assistance.
 
-### Out of Memory Issues
-
-The models are large, VRAM is expensive, and you may find yourself
-faced with Out of Memory errors when generating images. Here are some
-tips to reduce the problem:
-
-* **4 GB of VRAM**
-
-This should be adequate for 512x512 pixel images using Stable Diffusion 1.5
-and derived models, provided that you **disable** the NSFW checker. To
-disable the filter, do one of the following:
-
-   * Select option (6) "_change InvokeAI startup options_" from the
-     launcher. This will bring up the console-based startup settings
-     dialogue and allow you to unselect the "NSFW Checker" option.
-   * Start the startup settings dialogue directly by running
-     `invokeai-configure --skip-sd-weights --skip-support-models`
-     from the command line.
-   * Find the `invokeai.init` initialization file in the InvokeAI root
-     directory, open it in a text editor, and change `--nsfw_checker`
-     to `--no-nsfw_checker`
-
-If you are on a CUDA system, you can realize significant memory
-savings by activating the `xformers` library as described above. The
-downside is `xformers` introduces non-deterministic behavior, such
-that images generated with exactly the same prompt and settings will
-be slightly different from each other. See above for more information.
-
-* **6 GB of VRAM**
-
-This is a border case. Using the SD 1.5 series you should be able to
-generate images up to 640x640 with the NSFW checker enabled, and up to
-1024x1024 with it disabled and `xformers` activated. 
-
-If you run into persistent memory issues there are a series of
-environment variables that you can set before launching InvokeAI that
-alter how the PyTorch machine learning library manages memory.  See
-https://pytorch.org/docs/stable/notes/cuda.html#memory-management for
-a list of these tweaks.
-
-* **12 GB of VRAM**
-
-This should be sufficient to generate larger images up to about
-1280x1280. If you wish to push further, consider activating
-`xformers`.
-
-### Other Problems
+### other problems
 
 If you run into problems during or after installation, the InvokeAI team is
 available to help you. Either create an
@@ -501,20 +284,36 @@ hours, and often much sooner.
 
 ## Updating to newer versions
 
-This distribution is changing rapidly, and we add new features
-regularly. Releases are announced at
-http://github.com/invoke-ai/InvokeAI/releases, and at
-https://pypi.org/project/InvokeAI/ To update to the latest released
-version (recommended), follow these steps:
+This distribution is changing rapidly, and we add new features on a daily basis.
+To update to the latest released version (recommended), run the `update.sh`
+(Linux/Mac) or `update.bat` (Windows) scripts. This will fetch the latest
+release and re-run the `invokeai-configure` script to download any updated
+models files that may be needed. You can also use this to add additional models
+that you did not select at installation time.
 
-1. Start the `invoke.sh`/`invoke.bat` launch script from within the
-   `invokeai` root directory.
+You can now close the developer console and run `invoke` as before. If you get
+complaints about missing models, then you may need to do the additional step of
+running `invokeai-configure`. This happens relatively infrequently. To do
+this, simply open up the developer's console again and type
+`invokeai-configure`.
 
-2. Choose menu item (10) "Update InvokeAI".
+You may also use the `update` script to install any selected version of
+InvokeAI. From https://github.com/invoke-ai/InvokeAI, navigate to the zip file
+link of the version you wish to install. You can find the zip links by going to
+the one of the release pages and looking for the **Assets** section at the
+bottom. Alternatively, you can browse "branches" and "tags" at the top of the
+big code directory on the InvokeAI welcome page. When you find the version you
+want to install, go to the green "&lt;&gt; Code" button at the top, and copy the
+"Download ZIP" link.
 
-3. This will launch a menu that gives you the option of:
+Now run `update.sh` (or `update.bat`) with the version number of the desired InvokeAI
+version as its argument. For example, this will install the old 2.2.0 release.
 
-   1. Updating to the latest official release;
-   2. Updating to the bleeding-edge development version; or
-   3. Manually entering the tag or branch name of a version of
-      InvokeAI you wish to try out.
+```cmd
+update.sh v2.2.0
+```
+
+You can get the list of version numbers by going to the [releases
+page](https://github.com/invoke-ai/InvokeAI/releases) or by browsing
+the (Tags)[https://github.com/invoke-ai/InvokeAI/tags] list from the
+Code section of the main github page.

docs/installation/020_INSTALL_MANUAL.md

@@ -14,56 +14,17 @@ title: Installing Manually
 
 ## Introduction
 
-!!! tip "Conda"
-    As of InvokeAI v2.3.0 installation using the `conda` package manager is no longer being supported. It will likely still work, but we are not testing this installation method.
+!!! tip As of InvokeAI v2.3.0 installation using the `conda` package manager
+is no longer being supported. It will likely still work, but we are not testing
+this installation method.
 
 On Windows systems, you are encouraged to install and use the
 [PowerShell](https://learn.microsoft.com/en-us/powershell/scripting/install/installing-powershell-on-windows?view=powershell-7.3),
-which provides compatibility with Linux and Mac shells and nice
-features such as command-line completion.
+which provides compatibility with Linux and Mac shells and nice features such as
+command-line completion.
 
-### Prerequisites
-
-Before you start, make sure you have the following preqrequisites
-installed.  These are described in more detail in [Automated
-Installation](010_INSTALL_AUTOMATED.md), and in many cases will
-already be installed (if, for example, you have used your system for
-gaming):
-
-* **Python**
-
-    version 3.9 or 3.10 (3.11 is not recommended).
-
-* **CUDA Tools**
-
-    For those with _NVidia GPUs_, you will need to
-    install the [CUDA toolkit and optionally the XFormers library](070_INSTALL_XFORMERS.md).
-
-* **ROCm Tools**
-
-    For _Linux users with AMD GPUs_, you will need
-    to install the [ROCm toolkit](./030_INSTALL_CUDA_AND_ROCM.md). Note that
-    InvokeAI does not support AMD GPUs on Windows systems due to
-    lack of a Windows ROCm library.
-
-* **Visual C++ Libraries**
-
-    _Windows users_ must install the free
-    [Visual C++ libraries from Microsoft](https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170)
-
-* **The Xcode command line tools**
-
-    for _Macintosh users_. Instructions are available at
-    [Free Code Camp](https://www.freecodecamp.org/news/install-xcode-command-line-tools/)
-
-    * _Macintosh users_ may also need to run the `Install Certificates` command
-      if model downloads give lots of certificate errors. Run:
-      `/Applications/Python\ 3.10/Install\ Certificates.command`
-
-### Installation Walkthrough
-
-To install InvokeAI with virtual environments and the PIP package
-manager, please follow these steps:
+To install InvokeAI with virtual environments and the PIP package manager,
+please follow these steps:
 
 1.  Please make sure you are using Python 3.9 or 3.10. The rest of the install
     procedure depends on this and will not work with other versions:
@@ -72,127 +33,74 @@ manager, please follow these steps:
     python -V
     ```
 
-2.  Create a directory to contain your InvokeAI library, configuration
-    files, and models. This is known as the "runtime" or "root"
-    directory, and often lives in your home directory under the name `invokeai`.
+2.  Clone the [InvokeAI](https://github.com/invoke-ai/InvokeAI) source code from
+    GitHub:
 
-    Please keep in mind the disk space requirements - you will need at
-    least 20GB for the models and the virtual environment.  From now
-    on we will refer to this directory as `INVOKEAI_ROOT`. For convenience,
-    the steps below create a shell variable of that name which contains the
-    path to `HOME/invokeai`.
-
-    === "Linux/Mac"
-
-        ```bash
-        export INVOKEAI_ROOT=~/invokeai
-        mkdir $INVOKEAI_ROOT
-        ```
-
-    === "Windows (Powershell)"
-
-        ```bash
-        Set-Variable -Name INVOKEAI_ROOT -Value $Home/invokeai
-        mkdir $INVOKEAI_ROOT
-        ```
-
-3. Enter the root (invokeai) directory and create a virtual Python
-   environment within it named `.venv`. If the command `python`
-   doesn't work, try `python3`. Note that while you may create the
-   virtual environment anywhere in the file system, we recommend that
-   you create it within the root directory as shown here. This makes
-   it possible for the InvokeAI applications to find the model data
-   and configuration. If you do not choose to install the virtual
-   environment inside the root directory, then you **must** set the
-   `INVOKEAI_ROOT` environment variable in your shell environment, for
-   example, by editing `~/.bashrc` or `~/.zshrc` files, or setting the
-   Windows environment variable using the Advanced System Settings dialogue.
-   Refer to your operating system documentation for details.
-
-    ```terminal
-    cd $INVOKEAI_ROOT
-    python -m venv .venv --prompt InvokeAI
+    ```bash
+    git clone https://github.com/invoke-ai/InvokeAI.git
     ```
 
-4.  Activate the new environment:
+    This will create InvokeAI folder where you will follow the rest of the
+    steps.
 
-    === "Linux/Mac"
+3.  Create a directory of to contain your InvokeAI installation (known as the "runtime"
+    or "root" directory). This is where your models, configs, and outputs will live
+    by default. Please keep in mind the disk space requirements - you will need at
+    least 18GB (as of this writing) for the models and the virtual environment.
+    From now on we will refer to this directory as `INVOKEAI_ROOT`. This keeps the
+    runtime directory separate from the source code and aids in updating.
 
-        ```bash
-        source .venv/bin/activate
-        ```
+    ```bash
+    export INVOKEAI_ROOT="~/invokeai"
+    mkdir ${INVOKEAI_ROOT}
+    ```
 
-    === "Windows"
+4.  From within the InvokeAI top-level directory, create and activate a virtual
+    environment named `.venv` and prompt displaying `InvokeAI`:
 
-        ```ps
-        .venv\Scripts\activate
-        ```
+    ```bash
+    python -m venv ${INVOKEAI_ROOT}/.venv \
+        --prompt invokeai \
+        --upgrade-deps \
+        --copies
+    source ${INVOKEAI_ROOT}/.venv/bin/activate
+    ```
 
-        If you get a permissions error at this point, run this command and try again
+    !!! warning
 
-        `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser`
+        You **may** create your virtual environment anywhere on the filesystem.
+        But IF you choose a location that is *not* inside the `$INVOKEAI_ROOT` directory,
+        then you must set the `INVOKEAI_ROOT` environment variable in your shell environment,
+        for example, by editing `~/.bashrc` or `~/.zshrc` files, or setting the Windows environment
+        variable. Refer to your operating system / shell documentation for the correct way of doing so.
 
-    The command-line prompt should change to to show `(InvokeAI)` at the
-    beginning of the prompt. Note that all the following steps should be
-    run while inside the INVOKEAI_ROOT directory
-
-5.  Make sure that pip is installed in your virtual environment and up to date:
+5.  Make sure that pip is installed in your virtual environment an up to date:
 
     ```bash
     python -m pip install --upgrade pip
     ```
 
-6. Install the InvokeAI Package. The `--extra-index-url` option is used to select among
-   CUDA, ROCm and CPU/MPS drivers as shown below:
+6.  Install Package
 
-    === "CUDA (NVidia)"
+    ```bash
+    pip install --use-pep517 .
+    ```
 
-        ```bash
-        pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
-        ```
-
-    === "ROCm (AMD)"
-
-        ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
-        ```
-
-    === "CPU (Intel Macs & non-GPU systems)"
-
-        ```bash
-        pip install InvokeAI --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-        ```
-
-    === "MPS (M1 and M2 Macs)"
-
-        ```bash
-        pip install InvokeAI --use-pep517
-        ```
-
-7.  Deactivate and reactivate your runtime directory so that the invokeai-specific commands
+    Deactivate and reactivate your runtime directory so that the invokeai-specific commands
     become available in the environment
 
-    === "Linux/Macintosh"
+    ```
+    deactivate && source ${INVOKEAI_ROOT}/.venv/bin/activate
+    ```
 
-        ```bash
-        deactivate && source .venv/bin/activate
-        ```
-
-    === "Windows"
-
-        ```ps
-        deactivate
-        .venv\Scripts\activate
-        ```
-
-8.  Set up the runtime directory
+7.  Set up the runtime directory
 
     In this step you will initialize your runtime directory with the downloaded
     models, model config files, directory for textual inversion embeddings, and
     your outputs.
 
-    ```terminal
-    invokeai-configure
+    ```bash
+    invokeai-configure --root ${INVOKEAI_ROOT}
     ```
 
     The script `invokeai-configure` will interactively guide you through the
@@ -211,36 +119,35 @@ manager, please follow these steps:
         If you have already downloaded the weights file(s) for another Stable
         Diffusion distribution, you may skip this step (by selecting "skip" when
         prompted) and configure InvokeAI to use the previously-downloaded files. The
-        process for this is described in [Installing Models](050_INSTALLING_MODELS.md).
+        process for this is described in [here](050_INSTALLING_MODELS.md).
 
-9.  Run the command-line- or the web- interface:
+7.  Run the command-line- or the web- interface:
 
-    From within INVOKEAI_ROOT, activate the environment
-    (with `source .venv/bin/activate` or `.venv\scripts\activate`), and then run
-    the script `invokeai`. If the virtual environment you selected is NOT inside
-    INVOKEAI_ROOT, then you must specify the path to the root directory by adding
-    `--root_dir \path\to\invokeai` to the commands below:
+    Activate the environment (with `source .venv/bin/activate`), and then run
+    the script `invokeai`. If you selected a non-default location for the
+    runtime directory, please specify the path with the `--root_dir` option
+    (abbreviated below as `--root`):
 
     !!! example ""
 
-        !!! warning "Make sure that the virtual environment is activated, which should create `(.venv)` in front of your prompt!"
+        !!! warning "Make sure that the virtual environment is activated, which should create `(invokeai)` in front of your prompt!"
 
         === "CLI"
 
             ```bash
-            invokeai
+            invokeai --root ~/invokeai
             ```
 
         === "local Webserver"
 
             ```bash
-            invokeai --web
+            invokeai --web --root ~/invokeai
             ```
 
         === "Public Webserver"
 
             ```bash
-            invokeai --web --host 0.0.0.0
+            invokeai --web --host 0.0.0.0 --root ~/invokeai
             ```
 
         If you choose the run the web interface, point your browser at
@@ -248,122 +155,23 @@ manager, please follow these steps:
 
     !!! tip
 
-        You can permanently set the location of the runtime directory
-        by setting the environment variable `INVOKEAI_ROOT` to the
-        path of the directory. As mentioned previously, this is
-        *highly recommended** if your virtual environment is located outside of
-        your runtime directory.
+        You can permanently set the location of the runtime directory by setting the environment variable `INVOKEAI_ROOT` to the path of the directory. As mentioned previously, this is
+        **required** if your virtual environment is located outside of your runtime directory.
 
-10.  Render away!
+8.  Render away!
 
-    Browse the [features](../features/index.md) section to learn about all the
+    Browse the [features](../features/CLI.md) section to learn about all the
     things you can do with InvokeAI.
 
+    Note that some GPUs are slow to warm up. In particular, when using an AMD
+    card with the ROCm driver, you may have to wait for over a minute the first
+    time you try to generate an image. Fortunately, after the warm-up period
+    rendering will be fast.
 
-11.  Subsequently, to relaunch the script, activate the virtual environment, and
+9.  Subsequently, to relaunch the script, activate the virtual environment, and
     then launch `invokeai` command. If you forget to activate the virtual
     environment you will most likeley receive a `command not found` error.
 
     !!! warning
 
-        Do not move the runtime directory after installation. The virtual environment will get confused if the directory is moved.
-
-12. Other scripts
-
-    The [Textual Inversion](../features/TRAINING.md) script can be launched with the command:
-
-    ```bash
-    invokeai-ti --gui
-    ```
-
-    Similarly, the [Model Merging](../features/MODEL_MERGING.md) script can be launched with the command:
-
-    ```bash
-    invokeai-merge --gui
-    ```
-
-    Leave off the `--gui` option to run the script using command-line arguments. Pass the `--help` argument
-    to get usage instructions.
-
-### Developer Install
-
-If you have an interest in how InvokeAI works, or you would like to
-add features or bugfixes, you are encouraged to install the source
-code for InvokeAI. For this to work, you will need to install the
-`git` source code management program. If it is not already installed
-on your system, please see the [Git Installation
-Guide](https://github.com/git-guides/install-git)
-
-1. From the command line, run this command:
-   ```bash
-   git clone https://github.com/invoke-ai/InvokeAI.git
-   ```
-
-    This will create a directory named `InvokeAI` and populate it with the
-    full source code from the InvokeAI repository.
-
-2. Activate the InvokeAI virtual environment as per step (4) of the manual
-installation protocol (important!)
-
-3. Enter the InvokeAI repository directory and run one of these
-   commands, based on your GPU:
-
-    === "CUDA (NVidia)"
-        ```bash
-        pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
-        ```
-
-    === "ROCm (AMD)"
-        ```bash
-        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/rocm5.4.2
-        ```
-
-    === "CPU (Intel Macs & non-GPU systems)"
-        ```bash
-        pip install -e . --use-pep517 --extra-index-url https://download.pytorch.org/whl/cpu
-        ```
-
-    === "MPS (M1 and M2 Macs)"
-        ```bash
-        pip install -e . --use-pep517
-        ```
-
-    Be sure to pass `-e` (for an editable install) and don't forget the
-    dot ("."). It is part of the command.
-
-    You can now run `invokeai` and its related commands. The code will be
-    read from the repository, so that you can edit the .py source files
-    and watch the code's behavior change.
-
-4.  If you wish to contribute to the InvokeAI project, you are
-    encouraged to establish a GitHub account and "fork"
-    https://github.com/invoke-ai/InvokeAI into your own copy of the
-    repository. You can then use GitHub functions to create and submit
-    pull requests to contribute improvements to the project.
-
-    Please see [Contributing](../index.md#contributing) for hints
-    on getting started.
-
-### Unsupported Conda Install
-
-Congratulations, you found the "secret" Conda installation
-instructions. If you really **really** want to use Conda with InvokeAI
-you can do so using this unsupported recipe:
-
-```
-mkdir ~/invokeai
-conda create -n invokeai python=3.10
-conda activate invokeai
-pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu117
-invokeai-configure --root ~/invokeai
-invokeai --root ~/invokeai --web
-```
-
-The `pip install` command shown in this recipe is for Linux/Windows
-systems with an NVIDIA GPU. See step (6) above for the command to use
-with other platforms/GPU combinations. If you don't wish to pass the
-`--root` argument to `invokeai` with each launch, you may set the
-environment variable INVOKEAI_ROOT to point to the installation directory.
-
-Note that if you run into problems with the Conda installation, the InvokeAI
-staff will **not** be able to help you out. Caveat Emptor!
+        Do not move the runtime directory after installation. The virtual environment has absolute paths in it that get confused if the directory is moved.

docs/installation/030_INSTALL_CUDA_AND_ROCM.md

@@ -1,125 +0,0 @@
----
-title: NVIDIA Cuda / AMD ROCm
----
-
-<figure markdown>
-
-# :simple-nvidia: CUDA | :simple-amd: ROCm
-
-</figure>
-
-In order for InvokeAI to run at full speed, you will need a graphics
-card with a supported GPU. InvokeAI supports NVidia cards via the CUDA
-driver on Windows and Linux, and AMD cards via the ROCm driver on Linux.
-
-## :simple-nvidia: CUDA
-
-### Linux and Windows Install
-
-If you have used your system for other graphics-intensive tasks, such
-as gaming, you may very well already have the CUDA drivers
-installed. To confirm, open up a command-line window and type:
-
-```
-nvidia-smi
-```
-
-If this command produces a status report on the GPU(s) installed on
-your system, CUDA is installed and you have no more work to do. If
-instead you get "command not found", or similar, then the driver will
-need to be installed.
-
-We strongly recommend that you install the CUDA Toolkit package
-directly from NVIDIA. **Do not try to install Ubuntu's
-nvidia-cuda-toolkit package. It is out of date and will cause
-conflicts among the NVIDIA driver and binaries.**
-
-Go to [CUDA Toolkit 11.7
-Downloads](https://developer.nvidia.com/cuda-11-7-0-download-archive),
-and use the target selection wizard to choose your operating system,
-hardware platform, and preferred installation method (e.g. "local"
-versus "network").
-
-This will provide you with a downloadable install file or, depending
-on your choices, a recipe for downloading and running a install shell
-script. Be sure to read and follow the full installation instructions.
-
-After an install that seems successful, you can confirm by again
-running `nvidia-smi` from the command line.
-
-### Linux Install with a Runtime Container
-
-On Linux systems, an alternative to installing CUDA Toolkit directly on
-your system is to run an NVIDIA software container that has the CUDA
-libraries already in place. This is recommended if you are already 
-familiar with containerization technologies such as Docker.
-
-For downloads and instructions, visit the [NVIDIA CUDA Container
-Runtime Site](https://developer.nvidia.com/nvidia-container-runtime)
-
-### Torch Installation
-
-When installing torch and torchvision manually with `pip`, remember to provide
-the argument `--extra-index-url
-https://download.pytorch.org/whl/cu117` as described in the [Manual
-Installation Guide](020_INSTALL_MANUAL.md).
-
-## :simple-amd: ROCm
-
-### Linux Install
-
-AMD GPUs are only supported on Linux platforms due to the lack of a
-Windows ROCm driver at the current time. Also be aware that support
-for newer AMD GPUs is spotty. Your mileage may vary.
-
-It is possible that the ROCm driver is already installed on your
-machine. To test, open up a terminal window and issue the following
-command:
-
-```
-rocm-smi
-```
-
-If you get a table labeled "ROCm System Management Interface" the
-driver is installed and you are done. If you get "command not found,"
-then the driver needs to be installed.
-
-Go to AMD's [ROCm Downloads
-Guide](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation_new.html#installation-methods)
-and scroll to the _Installation Methods_ section. Find the subsection
-for the install method for your preferred Linux distribution, and
-issue the commands given in the recipe.
-
-Annoyingly, the official AMD site does not have a recipe for the most
-recent version of Ubuntu, 22.04. However, this [community-contributed
-recipe](https://novaspirit.github.io/amdgpu-rocm-ubu22/) is reported
-to work well.
-
-After installation, please run `rocm-smi` a second time to confirm
-that the driver is present and the GPU is recognized. You may need to
-do a reboot in order to load the driver.
-
-### Linux Install with a ROCm-docker Container
-
-If you are comfortable with the Docker containerization system, then
-you can build a ROCm docker file. The source code and installation
-recipes are available
-[Here](https://github.com/RadeonOpenCompute/ROCm-docker/blob/master/quick-start.md)
-
-### Torch Installation
-
-When installing torch and torchvision manually with `pip`, remember to provide
-the argument `--extra-index-url
-https://download.pytorch.org/whl/rocm5.4.2` as described in the [Manual
-Installation Guide](020_INSTALL_MANUAL.md).
-
-This will be done automatically for you if you use the installer
-script.
-
-Be aware that the torch machine learning library does not seamlessly
-interoperate with all AMD GPUs and you may experience garbled images,
-black images, or long startup delays before rendering commences. Most
-of these issues can be solved by Googling for workarounds. If you have
-a problem and find a solution, please post an
-[Issue](https://github.com/invoke-ai/InvokeAI/issues) so that other
-users benefit and we can update this document.

docs/installation/040_INSTALL_DOCKER.md

@@ -16,6 +16,10 @@ title: Installing with Docker
 
     For general use, install locally to leverage your machine's GPU.
 
+!!! tip "For running on a cloud instance/service"
+
+    Check out the [Running InvokeAI in the cloud with Docker](#running-invokeai-in-the-cloud-with-docker) section below
+
 ## Why containers?
 
 They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
@@ -74,40 +78,38 @@ Some Suggestions of variables you may want to change besides the Token:
 
 <figure markdown>
 
-| Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description                                                                                                                                                                                       |
-| ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `HUGGING_FACE_HUB_TOKEN`                              | No default, but **required**!                  | This is the only **required** variable, without it you can't download the huggingface models                                                                                                      |
-| `REPOSITORY_NAME`                                     | The Basename of the Repo folder                | This name will used as the container repository/image name                                                                                                                                        |
-| `VOLUMENAME`                                          | `${REPOSITORY_NAME,,}_data`                    | Name of the Docker Volume where model files will be stored                                                                                                                                        |
-| `ARCH`                                                | arch of the build machine                      | Can be changed if you want to build the image for another arch                                                                                                                                    |
-| `CONTAINER_REGISTRY`                                  | ghcr.io                                        | Name of the Container Registry to use for the full tag                                                                                                                                            |
-| `CONTAINER_REPOSITORY`                                | `$(whoami)/${REPOSITORY_NAME}`                 | Name of the Container Repository                                                                                                                                                                  |
-| `CONTAINER_FLAVOR`                                    | `cuda`                                         | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
-| `CONTAINER_TAG`                                       | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}`   | The Container Repository / Tag which will be used                                                                                                                                                 |
-| `INVOKE_DOCKERFILE`                                   | `Dockerfile`                                   | The Dockerfile which should be built, handy for development                                                                                                                                       |
-| `PIP_EXTRA_INDEX_URL`                                 |                                                | If you want to use a custom pip-extra-index-url                                                                                                                                                   |
+| Environment-Variable | Default value                   | Description                                                                                  |
+| -------------------- | -----------------------------   | -------------------------------------------------------------------------------------------- |
+| `HUGGINGFACE_TOKEN`  | No default, but **required**!   | This is the only **required** variable, without it you can't download the huggingface models |
+| `REPOSITORY_NAME`    | The Basename of the Repo folder | This name will used as the container repository/image name                                   |
+| `VOLUMENAME`         | `${REPOSITORY_NAME,,}_data`     | Name of the Docker Volume where model files will be stored                                   |
+| `ARCH`               | arch of the build machine       | can be changed if you want to build the image for another arch                               |
+| `INVOKEAI_TAG`       | latest                          | the Container Repository / Tag which will be used                                            |
+| `PIP_REQUIREMENTS`   | `requirements-lin-cuda.txt`     | the requirements file to use (from `environments-and-requirements`)                          |
+| `CONTAINER_FLAVOR`   | cuda                            | the flavor of the image, which can be changed if you build f.e. with amd requirements file.  |
+| `INVOKE_DOCKERFILE`  | `docker-build/Dockerfile`       | the Dockerfile which should be built, handy for development                                  |
 
 </figure>
 
 #### Build the Image
 
-I provided a build script, which is located next to the Dockerfile in
-`docker/build.sh`. It can be executed from repository root like this:
+I provided a build script, which is located in `docker-build/build.sh` but still
+needs to be executed from the Repository root.
 
 ```bash
-./docker/build.sh
+./docker-build/build.sh
 ```
 
 The build Script not only builds the container, but also creates the docker
-volume if not existing yet.
+volume if not existing yet, or if empty it will just download the models.
 
 #### Run the Container
 
 After the build process is done, you can run the container via the provided
-`docker/run.sh` script
+`docker-build/run.sh` script
 
 ```bash
-./docker/run.sh
+./docker-build/run.sh
 ```
 
 When used without arguments, the container will start the webserver and provide
@@ -117,7 +119,7 @@ also do so.
 !!! example "run script example"
 
     ```bash
-    ./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
+    ./docker-build/run.sh "banana sushi" -Ak_lms -S42 -s10
     ```
 
     This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
@@ -128,18 +130,16 @@ also do so.
 
 ## Running the container on your GPU
 
-If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
-the container with an extra environment variable to enable GPU usage and have
-the process run much faster:
+If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running the container with an extra
+environment variable to enable GPU usage and have the process run much faster:
 
 ```bash
-GPU_FLAGS=all ./docker/run.sh
+GPU_FLAGS=all ./docker-build/run.sh
 ```
 
 This passes the `--gpus all` to docker and uses the GPU.
 
-If you don't have a GPU (or your host is not yet setup to use it) you will see a
-message like this:
+If you don't have a GPU (or your host is not yet setup to use it) you will see a message like this:
 
 `docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
 
@@ -147,8 +147,84 @@ You can use the full set of GPU combinations documented here:
 
 https://docs.docker.com/config/containers/resource_constraints/#gpu
 
-For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
-choose a specific device identified by a UUID.
+For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to choose a specific device identified by a UUID.
+
+## Running InvokeAI in the cloud with Docker
+
+We offer an optimized Ubuntu-based image that has been well-tested in cloud deployments. Note: it also works well locally on Linux x86_64 systems with an Nvidia GPU. It *may* also work on Windows under WSL2 and on Intel Mac (not tested).
+
+An advantage of this method is that it does not need any local setup or additional dependencies.
+
+See the `docker-build/Dockerfile.cloud` file to familizarize yourself with the image's content.
+
+### Prerequisites
+
+- a `docker` runtime
+- `make` (optional but helps for convenience)
+- Huggingface token to download models, or an existing InvokeAI runtime directory from a previous installation
+
+ Neither local Python nor any dependencies are required. If you don't have `make` (part of `build-essentials` on Ubuntu), or do not wish to install it, the commands from the `docker-build/Makefile` are readily adaptable to be executed directly.
+
+### Building and running the image locally
+
+1. Clone this repo and `cd docker-build`
+1. `make build` - this will build the image. (This does *not* require a GPU-capable system).
+1. _(skip this step if you already have a complete InvokeAI runtime directory)_
+    - `make configure` (This does *not* require a GPU-capable system)
+    - this will create a local cache of models and configs (a.k.a the _runtime dir_)
+    - enter your Huggingface token when prompted
+1. `make web`
+1. Open the `http://localhost:9090` URL in your browser, and enjoy the banana sushi!
+
+To use InvokeAI on the cli, run `make cli`. To open a Bash shell in the container for arbitraty advanced use, `make shell`.
+
+#### Building and running without `make`
+
+(Feel free to adapt paths such as `${HOME}/invokeai` to your liking, and modify the CLI arguments as necessary).
+
+!!! example "Build the image and configure the runtime directory"
+    ```Shell
+    cd docker-build
+
+    DOCKER_BUILDKIT=1 docker build -t local/invokeai:latest -f Dockerfile.cloud ..
+
+    docker run --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/configure_invokeai.py"
+    ```
+
+!!! example "Run the web server"
+    ```Shell
+    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai -p9090:9090 local/invokeai:latest
+    ```
+
+    Access the Web UI at http://localhost:9090
+
+!!! example "Run the InvokeAI interactive CLI"
+    ```
+    docker run --runtime=nvidia --gpus=all --rm -it -v ${HOME}/invokeai:/mnt/invokeai local/invokeai:latest -c "python scripts/invoke.py"
+    ```
+
+### Running the image in the cloud
+
+This image works anywhere you can run a container with a mounted Docker volume. You may either build this image on a cloud instance, or build and push it to your Docker registry. To manually run this on a cloud instance (such as AWS EC2, GCP or Azure VM):
+
+1. build this image either in the cloud (you'll need to pull the repo), or locally
+1. `docker tag` it as `your-registry/invokeai` and push to your registry (i.e. Dockerhub)
+1. `docker pull` it on your cloud instance
+1. configure the runtime directory as per above example, using `docker run ... configure_invokeai.py` script
+1. use either one of the `docker run` commands above, substituting the image name for your own image.
+
+To run this on Runpod, please refer to the following Runpod template: https://www.runpod.io/console/gpu-secure-cloud?template=vm19ukkycf (you need a Runpod subscription). When launching the template, feel free to set the image to pull your own build.
+
+The template's `README` provides ample detail, but at a high level, the process is as follows:
+
+1. create a pod using this Docker image
+1. ensure the pod has an `INVOKEAI_ROOT=<path_to_your_persistent_volume>` environment variable, and that it corresponds to the path to your pod's persistent volume mount
+1. Run the pod with `sleep infinity` as the Docker command
+1. Use Runpod basic SSH to connect to the pod, and run `python scripts/configure_invokeai.py` script
+1. Stop the pod, and change the Docker command to `python scripts/invoke.py --web --host 0.0.0.0`
+1. Run the pod again, connect to your pod on HTTP port 9090, and enjoy the banana sushi!
+
+Running on other cloud providers such as Vast.ai will likely work in a similar fashion.
 
 ---
 
@@ -164,12 +240,13 @@ choose a specific device identified by a UUID.
 If you're on a **Linux container** the `invoke` script is **automatically
 started** and the output dir set to the Docker volume you created earlier.
 
-If you're **directly on macOS follow these startup instructions**. With the
-Conda environment activated (`conda activate ldm`), run the interactive
+If you're **directly on macOS follow these startup instructions**.
+With the Conda environment activated (`conda activate ldm`), run the interactive
 interface that combines the functionality of the original scripts `txt2img` and
-`img2img`: Use the more accurate but VRAM-intensive full precision math because
-half-precision requires autocast and won't work. By default the images are saved
-in `outputs/img-samples/`.
+`img2img`:
+Use the more accurate but VRAM-intensive full precision math because
+half-precision requires autocast and won't work.
+By default the images are saved in `outputs/img-samples/`.
 
 ```Shell
 python3 scripts/invoke.py --full_precision
@@ -185,9 +262,9 @@ invoke> q
 ### Text to Image
 
 For quick (but bad) image results test with 5 steps (default 50) and 1 sample
-image. This will let you know that everything is set up correctly. Then increase
-steps to 100 or more for good (but slower) results. The prompt can be in quotes
-or not.
+image. This will let you know that everything is set up correctly.
+Then increase steps to 100 or more for good (but slower) results.
+The prompt can be in quotes or not.
 
 ```Shell
 invoke> The hulk fighting with sheldon cooper -s5 -n1
@@ -200,9 +277,10 @@ You'll need to experiment to see if face restoration is making it better or
 worse for your specific prompt.
 
 If you're on a container the output is set to the Docker volume. You can copy it
-wherever you want. You can download it from the Docker Desktop app, Volumes,
-my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
-`docker cp` can't expand `*.png` so you'll need to specify the image file name.
+wherever you want.
+You can download it from the Docker Desktop app, Volumes, my-vol, data.
+Or you can copy it from your Mac terminal. Keep in mind `docker cp` can't expand
+`*.png` so you'll need to specify the image file name.
 
 On your host Mac (you can use the name of any container that mounted the
 volume):

docs/installation/050_INSTALLING_MODELS.md

@@ -4,170 +4,249 @@ title: Installing Models
 
 # :octicons-paintbrush-16: Installing Models
 
-## Checkpoint and Diffusers Models
+## Model Weight Files
 
-The model checkpoint files ('\*.ckpt') are the Stable Diffusion
-"secret sauce". They are the product of training the AI on millions of
-captioned images gathered from multiple sources.
+The model weight files ('\*.ckpt') are the Stable Diffusion "secret sauce". They
+are the product of training the AI on millions of captioned images gathered from
+multiple sources.
 
-Originally there was only a single Stable Diffusion weights file,
-which many people named `model.ckpt`. Now there are dozens or more
-that have been fine tuned to provide particulary styles, genres, or
-other features. In addition, there are several new formats that
-improve on the original checkpoint format: a `.safetensors` format
-which prevents malware from masquerading as a model, and `diffusers`
-models, the most recent innovation.
+Originally there was only a single Stable Diffusion weights file, which many
+people named `model.ckpt`. Now there are dozens or more that have been "fine
+tuned" to provide particulary styles, genres, or other features. InvokeAI allows
+you to install and run multiple model weight files and switch between them
+quickly in the command-line and web interfaces.
 
-InvokeAI supports all three formats but strongly prefers the
-`diffusers` format. These are distributed as directories containing
-multiple subfolders, each of which contains a different aspect of the
-model. The advantage of this is that the models load from disk really
-fast. Another advantage is that `diffusers` models are supported by a
-large and active set of open source developers working at and with
-HuggingFace organization, and improvements in both rendering quality
-and performance are being made at a rapid pace. Among other features
-is the ability to download and install a `diffusers` model just by
-providing its HuggingFace repository ID.
-
-While InvokeAI will continue to support `.ckpt` and `.safetensors`
-models for the near future, these are deprecated and support will
-likely be withdrawn at some point in the not-too-distant future.
-
-This manual will guide you through installing and configuring model
-weight files and converting legacy `.ckpt` and `.safetensors` files
-into performant `diffusers` models.
+This manual will guide you through installing and configuring model weight
+files.
 
 ## Base Models
 
-InvokeAI comes with support for a good set of starter models. You'll
-find them listed in the master models file
-`configs/INITIAL_MODELS.yaml` in the InvokeAI root directory. The
-subset that are currently installed are found in
-`configs/models.yaml`.
+InvokeAI comes with support for a good initial set of models listed in the model
+configuration file `configs/models.yaml`. They are:
 
-Note that these files are covered by an "Ethical AI" license which
-forbids certain uses. When you initially download them, you are asked
-to accept the license terms. In addition, some of these models carry
-additional license terms that limit their use in commercial
-applications or on public servers. Be sure to familiarize yourself
-with the model terms by visiting the URLs in the table above.
+| Model                | Weight File                       | Description                                                | DOWNLOAD FROM                                                  |
+| -------------------- | --------------------------------- | ---------------------------------------------------------- | -------------------------------------------------------------- |
+| stable-diffusion-1.5 | v1-5-pruned-emaonly.ckpt          | Most recent version of base Stable Diffusion model         | https://huggingface.co/runwayml/stable-diffusion-v1-5          |
+| stable-diffusion-1.4 | sd-v1-4.ckpt                      | Previous version of base Stable Diffusion model            | https://huggingface.co/CompVis/stable-diffusion-v-1-4-original |
+| inpainting-1.5       | sd-v1-5-inpainting.ckpt           | Stable Diffusion 1.5 model specialized for inpainting      | https://huggingface.co/runwayml/stable-diffusion-inpainting    |
+| waifu-diffusion-1.3  | model-epoch09-float32.ckpt        | Stable Diffusion 1.4 trained to produce anime images       | https://huggingface.co/hakurei/waifu-diffusion-v1-3            |
+| `<all models>`       | vae-ft-mse-840000-ema-pruned.ckpt | A fine-tune file add-on file that improves face generation | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/     |
+
+Note that these files are covered by an "Ethical AI" license which forbids
+certain uses. You will need to create an account on the Hugging Face website and
+accept the license terms before you can access the files.
+
+The predefined configuration file for InvokeAI (located at
+`configs/models.yaml`) provides entries for each of these weights files.
+`stable-diffusion-1.5` is the default model used, and we strongly recommend that
+you install this weights file if nothing else.
 
 ## Community-Contributed Models
 
-[HuggingFace](https://huggingface.co/models?library=diffusers)
-is a great resource for diffusers models, and is also the home of a
-[fast-growing repository](https://huggingface.co/sd-concepts-library)
-of embedding (".bin") models that add subjects and/or styles to your
-images. The latter are automatically installed on the fly when you
-include the text `<concept-name>` in your prompt. See [Concepts
-Library](../features/CONCEPTS.md) for more information.
+There are too many to list here and more are being contributed every day.
+Hugging Face maintains a
+[fast-growing repository](https://huggingface.co/sd-concepts-library) of
+fine-tune (".bin") models that can be imported into InvokeAI by passing the
+`--embedding_path` option to the `invoke.py` command.
 
-Another popular site for community-contributed models is
-[CIVITAI](https://civitai.com). This extensive site currently supports
-only `.safetensors` and `.ckpt` models, but they can be easily loaded
-into InvokeAI and/or converted into optimized `diffusers` models. Be
-aware that CIVITAI hosts many models that generate NSFW content.
+[This page](https://rentry.org/sdmodels) hosts a large list of official and
+unofficial Stable Diffusion models and where they can be obtained.
 
 ## Installation
 
-There are two ways to install and manage models:
+There are three ways to install weights files:
 
-1. The `invokeai-model-install` script which will download and install
-them for you.  In addition to supporting main models, you can install
-ControlNet, LoRA and Textual Inversion models.
+1. During InvokeAI installation, the `invokeai-configure` script can download
+   them for you.
 
-2. The web interface (WebUI) has a GUI for importing and managing
-   models.
+2. You can use the command-line interface (CLI) to import, configure and modify
+   new models files.
 
-3. By placing models (or symbolic links to models) inside one of the
-InvokeAI root directory's `autoimport` folder.
+3. You can download the files manually and add the appropriate entries to
+   `models.yaml`.
 
-### Installation via `invokeai-model-install`
+### Installation via `invokeai-configure`
 
-From the `invoke` launcher, choose option [5] "Download and install
-models." This will launch the same script that prompted you to select
-models at install time. You can use this to add models that you
-skipped the first time around. It is all right to specify a model that
-was previously downloaded; the script will just confirm that the files
-are complete.
+This is the most automatic way. Run `invokeai-configure` from the
+console. It will ask you to select which models to download and lead you through
+the steps of setting up a Hugging Face account if you haven't done so already.
 
-The installer has different panels for installing main models from
-HuggingFace, models from Civitai and other arbitrary web sites,
-ControlNet models, LoRA/LyCORIS models, and Textual Inversion
-embeddings. Each section has a text box in which you can enter a new
-model to install. You can refer to a model using its:
+To start, run `invokeai-configure` from within the InvokeAI:
+directory
 
-1. Local path to the .ckpt, .safetensors or diffusers folder on your local machine
-2. A directory on your machine that contains multiple models
-3. A URL that points to a downloadable model
-4. A HuggingFace repo id
+!!! example ""
 
-Previously-installed models are shown with checkboxes. Uncheck a box
-to unregister the model from InvokeAI. Models that are physically
-installed inside the InvokeAI root directory will be deleted and
-purged (after a confirmation warning). Models that are located outside
-the InvokeAI root directory will be unregistered but not deleted.
+    ```text
+    Loading Python libraries...
 
-Note: The installer script uses a console-based text interface that requires
-significant amounts of horizontal and vertical space. If the display
-looks messed up, just enlarge the terminal window and/or relaunch the
-script.
+    ** INTRODUCTION **
+    Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
+    and other large models that are needed for text to image generation. At any point you may interrupt
+    this program and resume later.
 
-If you wish you can script model addition and deletion, as well as
-listing installed models. Start the "developer's console" and give the
-command `invokeai-model-install --help`. This will give you a series
-of command-line parameters that will let you control model
-installation. Examples:
+    ** WEIGHT SELECTION **
+    Would you like to download the Stable Diffusion model weights now? [y]
 
-```
-# (list all controlnet models)
-invokeai-model-install --list controlnet
+    Choose the weight file(s) you wish to download. Before downloading you
+    will be given the option to view and change your selections.
 
-# (install the model at the indicated URL)
-invokeai-model-install --add http://civitai.com/2860
+    [1] stable-diffusion-1.5:
+        The newest Stable Diffusion version 1.5 weight file (4.27 GB) (recommended)
+        Download? [y]
+    [2] inpainting-1.5:
+        RunwayML SD 1.5 model optimized for inpainting (4.27 GB) (recommended)
+        Download? [y]
+    [3] stable-diffusion-1.4:
+        The original Stable Diffusion version 1.4 weight file (4.27 GB)
+        Download? [n] n
+    [4] waifu-diffusion-1.3:
+        Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
+        Download? [n] y
+    [5] ft-mse-improved-autoencoder-840000:
+        StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB) (recommended)
+        Download? [y] y
+    The following weight files will be downloaded:
+      [1] stable-diffusion-1.5*
+      [2] inpainting-1.5
+      [4] waifu-diffusion-1.3
+      [5] ft-mse-improved-autoencoder-840000
+    *default
+    Ok to download? [y]
+    ** LICENSE AGREEMENT FOR WEIGHT FILES **
 
-# (delete the named model)
-invokeai-model-install --delete sd-1/main/analog-diffusion
+    1. To download the Stable Diffusion weight files you need to read and accept the
+      CreativeML Responsible AI license. If you have not already done so, please
+      create an account using the "Sign Up" button:
+
+      https://huggingface.co
+
+      You will need to verify your email address as part of the HuggingFace
+      registration process.
+
+    2. After creating the account, login under your account and accept
+      the license terms located here:
+
+      https://huggingface.co/CompVis/stable-diffusion-v-1-4-original
+
+    Press <enter> when you are ready to continue:
+    ...
+    ```
+
+When the script is complete, you will find the downloaded weights files in
+`models/ldm/stable-diffusion-v1` and a matching configuration file in
+`configs/models.yaml`.
+
+You can run the script again to add any models you didn't select the first time.
+Note that as a safety measure the script will _never_ remove a
+previously-installed weights file. You will have to do this manually.
+
+### Installation via the CLI
+
+You can install a new model, including any of the community-supported ones, via
+the command-line client's `!import_model` command.
+
+1.  First download the desired model weights file and place it under
+    `models/ldm/stable-diffusion-v1/`. You may rename the weights file to
+    something more memorable if you wish. Record the path of the weights file
+    (e.g. `models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`)
+
+2.  Launch the `invoke.py` CLI with `python scripts/invoke.py`.
+
+3.  At the `invoke>` command-line, enter the command
+    `!import_model <path to model>`. For example:
+
+    `invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+
+    !!! tip "the CLI supports file path autocompletion"
+
+         Type a bit of the path name and hit ++tab++ in order to get a choice of
+         possible completions.
+
+    !!! tip "on Windows, you can drag model files onto the command-line"
+
+         Once you have typed in `!import_model `, you can drag the model `.ckpt` file
+         onto the command-line to insert the model path. This way, you don't need to
+         type it or copy/paste.
+
+4.  Follow the wizard's instructions to complete installation as shown in the
+    example here:
+
+    !!! example ""
+
+        ```text
+        invoke> !import_model models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+        >> Model import in process. Please enter the values needed to configure this model:
+
+        Name for this model: arabian-nights
+        Description of this model: Arabian Nights Fine Tune v1.0
+        Configuration file for this model: configs/stable-diffusion/v1-inference.yaml
+        Default image width: 512
+        Default image height: 512
+        >> New configuration:
+        arabian-nights:
+          config: configs/stable-diffusion/v1-inference.yaml
+          description: Arabian Nights Fine Tune v1.0
+          height: 512
+          weights: models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+          width: 512
+        OK to import [n]? y
+        >> Caching model stable-diffusion-1.4 in system RAM
+        >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+          | LatentDiffusion: Running in eps-prediction mode
+          | DiffusionWrapper has 859.52 M params.
+          | Making attention of type 'vanilla' with 512 in_channels
+          | Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
+          | Making attention of type 'vanilla' with 512 in_channels
+          | Using faster float16 precision
+        ```
+
+If you've previously installed the fine-tune VAE file
+`vae-ft-mse-840000-ema-pruned.ckpt`, the wizard will also ask you if you want to
+add this VAE to the model.
+
+The appropriate entry for this model will be added to `configs/models.yaml` and
+it will be available to use in the CLI immediately.
+
+The CLI has additional commands for switching among, viewing, editing, deleting
+the available models. These are described in
+[Command Line Client](../features/CLI.md#model-selection-and-importation), but
+the two most frequently-used are `!models` and `!switch <name of model>`. The
+first prints a table of models that InvokeAI knows about and their load status.
+The second will load the requested model and lets you switch back and forth
+quickly among loaded models.
+
+### Manually editing of `configs/models.yaml`
+
+If you are comfortable with a text editor then you may simply edit `models.yaml`
+directly.
+
+First you need to download the desired .ckpt file and place it in
+`models/ldm/stable-diffusion-v1` as descirbed in step #1 in the previous
+section. Record the path to the weights file, e.g.
+`models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt`
+
+Then using a **text** editor (e.g. the Windows Notepad application), open the
+file `configs/models.yaml`, and add a new stanza that follows this model:
+
+```yaml
+arabian-nights-1.0:
+  description: A great fine-tune in Arabian Nights style
+  weights: ./models/ldm/stable-diffusion-v1/arabian-nights-1.0.ckpt
+  config: ./configs/stable-diffusion/v1-inference.yaml
+  width: 512
+  height: 512
+  vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+  default: false
 ```
 
-### Installation via the Web GUI
+| name               | description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| :----------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| arabian-nights-1.0 | This is the name of the model that you will refer to from within the CLI and the WebGUI when you need to load and use the model.                                                                                                                                                                                                                                                                                                                                                                                                  |
+| description        | Any description that you want to add to the model to remind you what it is.                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+| weights            | Relative path to the .ckpt weights file for this model.                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+| config             | This is the confusingly-named configuration file for the model itself. Use `./configs/stable-diffusion/v1-inference.yaml` unless the model happens to need a custom configuration, in which case the place you downloaded it from will tell you what to use instead. For example, the runwayML custom inpainting model requires the file `configs/stable-diffusion/v1-inpainting-inference.yaml`. This is already inclued in the InvokeAI distribution and is configured automatically for you by the `invokeai-configure` script. |
+| vae                | If you want to add a VAE file to the model, then enter its path here.                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+| width, height      | This is the width and height of the images used to train the model. Currently they are always 512 and 512.                                                                                                                                                                                                                                                                                                                                                                                                                        |
 
-To install a new model using the Web GUI, do the following:
-
-1. Open the InvokeAI Model Manager (cube at the bottom of the
-left-hand panel) and navigate to *Import Models*
-
-2. In the field labeled *Location* type in the path to the model you
-wish to install. You may use a URL, HuggingFace repo id, or a path on
-your local disk.
-
-3. Alternatively, the *Scan for Models* button allows you to paste in
-the path to a folder somewhere on your machine. It will be scanned for
-importable models and prompt you to add the ones of your choice.
-
-4. Press *Add Model* and wait for confirmation that the model
-was added.
-
-To delete a model, Select *Model Manager* to list all the currently
-installed models. Press the trash can icons to delete any models you
-wish to get rid of. Models whose weights are located inside the
-InvokeAI `models` directory will be purged from disk, while those
-located outside will be unregistered from InvokeAI, but not deleted.
-
-You can see where model weights are located by clicking on the model name.
-This will bring up an editable info panel showing the model's characteristics,
-including the `Model Location` of its files.
-
-### Installation via the `autoimport` function
-
-In the InvokeAI root directory you will find a series of folders under
-`autoimport`, one each for main models, controlnets, embeddings and
-Loras.  Any models that you add to these directories will be scanned
-at startup time and registered automatically.
-
-You may create symbolic links from these folders to models located
-elsewhere on disk and they will be autoimported. You can also create
-subfolders and organize them as you wish.
-
-The location of the autoimport directories are controlled by settings
-in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md).
+Save the `models.yaml` and relaunch InvokeAI. The new model should now be
+available for your use.

docs/installation/060_INSTALL_PATCHMATCH.md

@@ -24,7 +24,7 @@ You need to have opencv installed so that pypatchmatch can be built:
 brew install opencv
 ```
 
-The next time you start `invoke`, after successfully installing opencv, pypatchmatch will be built.
+The next time you start `invoke`, after sucesfully installing opencv, pypatchmatch will be built.
 
 ## Linux
 
@@ -56,7 +56,7 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 5. Confirm that pypatchmatch is installed. At the command-line prompt enter
    `python`, and then at the `>>>` line type
-   `from patchmatch import patch_match`: It should look like the following:
+   `from patchmatch import patch_match`: It should look like the follwing:
 
     ```py
     Python 3.9.5 (default, Nov 23 2021, 15:27:38)
@@ -87,18 +87,18 @@ Prior to installing PyPatchMatch, you need to take the following steps:
     sudo pacman -S --needed base-devel
     ```
 
-2. Install `opencv` and `blas`:
+2. Install `opencv`:
 
     ```sh
-    sudo pacman -S opencv blas
+    sudo pacman -S opencv
     ```
 
     or for CUDA support
 
     ```sh
-    sudo pacman -S opencv-cuda blas
+    sudo pacman -S opencv-cuda
     ```
-    
+
 3. Fix the naming of the `opencv` package configuration file:
 
     ```sh
@@ -108,4 +108,4 @@ Prior to installing PyPatchMatch, you need to take the following steps:
 
 [**Next, Follow Steps 4-6 from the Debian Section above**](#linux)
 
-If you see no errors you're ready to go!
+If you see no errors, then you're ready to go!

docs/installation/index.md

@@ -3,19 +3,7 @@ title: Overview
 ---
 
 We offer several ways to install InvokeAI, each one suited to your
-experience and preferences. We suggest that everyone start by
-reviewing the
-[hardware](010_INSTALL_AUTOMATED.md#hardware_requirements) and
-[software](010_INSTALL_AUTOMATED.md#software_requirements)
-requirements, as they are the same across each install method. Then
-pick the install method most suitable to your level of experience and
-needs.
-
-See the [troubleshooting
-section](010_INSTALL_AUTOMATED.md#troubleshooting) of the automated
-install guide for frequently-encountered installation issues.
-
-## Main Application
+experience and preferences.
 
 1. [Automated Installer](010_INSTALL_AUTOMATED.md)
 
@@ -30,8 +18,8 @@ install guide for frequently-encountered installation issues.
     InvokeAI and its dependencies. We offer two recipes: one suited to
     those who prefer the `conda` tool, and one suited to those who prefer
     `pip` and Python virtual environments. In our hands the pip install
-    is faster and more reliable, but your mileage may vary.
-    Note that the conda installation method is currently deprecated and
+    is faster and more reliable, but your mileage may vary. 
+    Note that the conda installation method is currently deprecated and 
     will not be supported at some point in the future.
 
     This method is recommended for users who have previously used `conda`
@@ -45,10 +33,3 @@ install guide for frequently-encountered installation issues.
     InvokeAI and its dependencies. This method is recommended for
     individuals with experience with Docker containers and understand
     the pluses and minuses of a container-based install.
-
-## Quick Guides
-
-* [Installing CUDA and ROCm Drivers](./030_INSTALL_CUDA_AND_ROCM.md)
-* [Installing XFormers](./070_INSTALL_XFORMERS.md)
-* [Installing PyPatchMatch](./060_INSTALL_PATCHMATCH.md)
-* [Installing New Models](./050_INSTALLING_MODELS.md)

docs/openapi3_0.yaml

@@ -0,0 +1,73 @@
+openapi: 3.0.3
+info:
+  title: Stable Diffusion
+  description: |-
+    TODO: Description Here
+
+    Some useful links:
+    - [Stable Diffusion Dream Server](https://github.com/lstein/stable-diffusion)
+
+  license:
+    name: MIT License
+    url: https://github.com/lstein/stable-diffusion/blob/main/LICENSE
+  version: 1.0.0
+servers:
+  - url: http://localhost:9090/api
+tags:
+  - name: images
+    description: Retrieve and manage generated images
+paths:
+  /images/{imageId}:
+    get:
+      tags:
+        - images
+      summary: Get image by ID
+      description: Returns a single image
+      operationId: getImageById
+      parameters:
+        - name: imageId
+          in: path
+          description: ID of image to return
+          required: true
+          schema:
+            type: string
+      responses:
+        '200':
+          description: successful operation
+          content:
+            image/png:
+              schema:
+                type: string
+                format: binary
+        '404':
+          description: Image not found
+  /intermediates/{intermediateId}/{step}:
+    get:
+      tags:
+        - images
+      summary: Get intermediate image by ID
+      description: Returns a single intermediate image
+      operationId: getIntermediateById
+      parameters:
+        - name: intermediateId
+          in: path
+          description: ID of intermediate to return
+          required: true
+          schema:
+            type: string
+        - name: step
+          in: path
+          description: The generation step of the intermediate
+          required: true
+          schema:
+            type: string
+      responses:
+        '200':
+          description: successful operation
+          content:
+            image/png:
+              schema:
+                type: string
+                format: binary
+        '404':
+          description: Intermediate not found

docs/other/CONTRIBUTORS.md

@@ -23,11 +23,9 @@ We thank them for all of their time and hard work.
 * @damian0815 - Attention Systems and Gameplay Engineer
 * @mauwii (Matthias Wild) - Continuous integration and product maintenance engineer
 * @Netsvetaev (Artur Netsvetaev) - UI/UX Developer
-* @tildebyte - General gadfly and resident (self-appointed) know-it-all
+* @tildebyte - general gadfly and resident (self-appointed) know-it-all
 * @keturn - Lead for Diffusers port
 * @ebr (Eugene Brodsky) - Cloud/DevOps/Sofware engineer; your friendly neighbourhood cluster-autoscaler
-* @jpphoto (Jonathan Pollack) - Inference and rendering engine optimization
-* @genomancer (Gregg Helt) - Model training and merging
 
 ## **Contributions by**
 

docs/other/TRANSLATION.md

@@ -1,19 +0,0 @@
-# Translation
-
-InvokeAI uses [Weblate](https://weblate.org) for translation. Weblate is a FOSS project providing a scalable translation service. Weblate automates the tedious parts of managing translation of a growing project, and the service is generously provided at no cost to FOSS projects like InvokeAI.
-
-## Contributing
-
-If you'd like to contribute by adding or updating a translation, please visit our [Weblate project](https://hosted.weblate.org/engage/invokeai/). You'll need to sign in with your GitHub account (a number of other accounts are supported, including Google).
-
-Once signed in, select a language and then the Web UI component. From here you can Browse and Translate strings from English to your chosen language. Zen mode offers a simpler translation experience.
-
-Your changes will be attributed to you in the automated PR process; you don't need to do anything else.
-
-## Help & Questions
-
-Please check Weblate's [documentation](https://docs.weblate.org/en/latest/index.html) or ping @psychedelicious or @blessedcoolant on Discord if you have any questions.
-
-## Thanks
-
-Thanks to the InvokeAI community for their efforts to translate the project!

docs/swagger-ui/index.css

@@ -0,0 +1,16 @@
+html {
+    box-sizing: border-box;
+    overflow: -moz-scrollbars-vertical;
+    overflow-y: scroll;
+}
+
+*,
+*:before,
+*:after {
+    box-sizing: inherit;
+}
+
+body {
+    margin: 0;
+    background: #fafafa;
+}

docs/swagger-ui/oauth2-redirect.html

@@ -0,0 +1,79 @@
+<!doctype html>
+<html lang="en-US">
+<head>
+    <title>Swagger UI: OAuth2 Redirect</title>
+</head>
+<body>
+<script>
+    'use strict';
+    function run () {
+        var oauth2 = window.opener.swaggerUIRedirectOauth2;
+        var sentState = oauth2.state;
+        var redirectUrl = oauth2.redirectUrl;
+        var isValid, qp, arr;
+
+        if (/code|token|error/.test(window.location.hash)) {
+            qp = window.location.hash.substring(1).replace('?', '&');
+        } else {
+            qp = location.search.substring(1);
+        }
+
+        arr = qp.split("&");
+        arr.forEach(function (v,i,_arr) { _arr[i] = '"' + v.replace('=', '":"') + '"';});
+        qp = qp ? JSON.parse('{' + arr.join() + '}',
+                function (key, value) {
+                    return key === "" ? value : decodeURIComponent(value);
+                }
+        ) : {};
+
+        isValid = qp.state === sentState;
+
+        if ((
+          oauth2.auth.schema.get("flow") === "accessCode" ||
+          oauth2.auth.schema.get("flow") === "authorizationCode" ||
+          oauth2.auth.schema.get("flow") === "authorization_code"
+        ) && !oauth2.auth.code) {
+            if (!isValid) {
+                oauth2.errCb({
+                    authId: oauth2.auth.name,
+                    source: "auth",
+                    level: "warning",
+                    message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
+                });
+            }
+
+            if (qp.code) {
+                delete oauth2.state;
+                oauth2.auth.code = qp.code;
+                oauth2.callback({auth: oauth2.auth, redirectUrl: redirectUrl});
+            } else {
+                let oauthErrorMsg;
+                if (qp.error) {
+                    oauthErrorMsg = "["+qp.error+"]: " +
+                        (qp.error_description ? qp.error_description+ ". " : "no accessCode received from the server. ") +
+                        (qp.error_uri ? "More info: "+qp.error_uri : "");
+                }
+
+                oauth2.errCb({
+                    authId: oauth2.auth.name,
+                    source: "auth",
+                    level: "error",
+                    message: oauthErrorMsg || "[Authorization failed]: no accessCode received from the server."
+                });
+            }
+        } else {
+            oauth2.callback({auth: oauth2.auth, token: qp, isValid: isValid, redirectUrl: redirectUrl});
+        }
+        window.close();
+    }
+
+    if (document.readyState !== 'loading') {
+        run();
+    } else {
+        document.addEventListener('DOMContentLoaded', function () {
+            run();
+        });
+    }
+</script>
+</body>
+</html>

docs/swagger-ui/swagger-initializer.js

@@ -0,0 +1,20 @@
+window.onload = function() {
+  //<editor-fold desc="Changeable Configuration Block">
+
+  // the following lines will be replaced by docker/configurator, when it runs in a docker-container
+  window.ui = SwaggerUIBundle({
+    url: "openapi3_0.yaml",
+    dom_id: '#swagger-ui',
+    deepLinking: true,
+    presets: [
+      SwaggerUIBundle.presets.apis,
+      SwaggerUIStandalonePreset
+    ],
+    plugins: [
+      SwaggerUIBundle.plugins.DownloadUrl
+    ],
+    layout: "StandaloneLayout"
+  });
+
+  //</editor-fold>
+};